Learner modeling in cloud computing
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Learner modeling in cloud computing Sameh Ghallabi 1 & Fathi Essalmi 1 & Mohamed Jemni 1,2 & Kinshuk 3 Received: 10 February 2020 / Accepted: 7 April 2020/ # Springer Science+Business Media, LLC, part of Springer Nature 2020
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). Keywords Learner modeling . personalized learning systems . learners' traces .
personalization parameters . SVM algorithm . Cloud computing
1 Introduction The learner model is an essential component of personalized learning systems. It contains personalization parameters that define the characteristics and needs of learners, such as interests, preferences, and levels of knowledge, goals, tasks, backgrounds,
* Sameh Ghallabi [email protected] Fathi Essalmi [email protected] Mohamed Jemni [email protected] Kinshuk [email protected] Extended author information available on the last page of the article
Education and Information Technologies
learning performances, learning styles, aptitudes and environments, as well as other useful features (Aljohany et al. 2018). In order to achieve the personalization of elearning courses, learning systems use different personalization parameters. In the literature, several approaches have used learner model in order to personalize learning (Ahmed et al. 2017; Khamis 2015). The goal of personalization is to fit the learners’ needs and their learning goals (Jalal and Mahmood 2019; Alsadoon 2020). The personalized learning systems limit the network and the space of their functioning. There is no sharable or interoperable platform capable of integrating the various existing systems. The users perform the mentioned systems from downloaded software on their computers or from a physical server situated in their buildings. Cloud computing presents a solution to meet such needs. It is a new technology that allows users to access applications through the Internet. It is a combination of several services: Software (SaaS), Platform (PaaS) and Infrastructure (IaaS) as a service. In literature, a lot of work has considered the benefits of the cloud for app
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