Knowledge tracing with an intelligent agent, in an e-learning platform

E-learning systems have gained nowadays a large student community due to the facility of use and the integration of one-to-one service. Indeed, the personalization of the learning process for every user is needed to increase the student satisfaction and learning efficiency. Nevertheless, the number of students who give up their learning process cannot be neglected. Therefore, it is mandatory to establish an efficient way to assess the level of personalization in such systems. In fact, assessing represents the evolution's key in every personalized application and especially for the e-learning systems. Besides, when the e-learning system can decipher the student personality, the student learning process will be stabilized, and the dropout rate will be decreased. In this context, we propose to evaluate the personalization process in an e-learning platform using an intelligent referential system based on agents. It evaluates any recommendation made by the e-learning platform based on a comparison. We compare the personalized service of the e-learning system and those provided by our referential system. Therefore, our purpose consists in increasing the efficiency of the proposed system to obtain a significant assessment result; precisely, the aim is to improve the outcomes of every algorithm used in each defined agent. This paper deals with the intelligent agent Mod-Knowledge' responsible for analyzing the student interaction to trace the student knowledge state. The originality of this agent is that it treats the external and the internal student interactions using machine learning algorithms to obtain a complete view of the student knowledge state. The validation of this contribution is done with experiments showing that the proposed algorithms outperform the existing ones.