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).

随着技术的出现，电子学习系统的个性化得到了增强。这些系统使用一组参数来个性化课程。但是，在文献中，这些参数不是基于分类和优化算法在云中实现的。云计算是一种新的计算模型，其中标准和虚拟化资源通过Internet作为服务提供。本文提出了一种允许在集成了这些参数的云中进行学习者建模的方法。建议的方法基于支持向量机算法，该算法分析学习者的踪迹，以低成本选择参数，从而找到学习者的最佳分类。进行实验以验证这种方法。该实验基于为学习者建模而产生的轨迹。获得的结果表明，与经典系统（无云）相比，该方法代表了具有较低运营成本的学习者模型。