Abstract:

Providing the right learning contents at the right time to each learner by considering his/her profile (knowledge, preferences, objectives…) is the main objective of adaptive educational systems. According to researchers in this field, in order to achieve a high level of adaptivity in any educational system, it is necessary to use artificial intelligence technologies and to more focus on student modeling. To help learners learn effectively and provide them with what they want automatically without waiting for them to ask, it is necessary to design an adequate dynamic student model for each one of them. For that reason, there are many techniques and approaches that we can use depending of the personalization parameter selected.

The present paper aims to present a comparative study of student modeling approaches and techniques from 2012 to 2017 and the personalization parameters to consider in the learner's profile. This paper also considers ways to combine different student modeling approaches to create a more robust student model in an adaptive educational system.

Keywords:

Student model, Adaptive Educational Systems, personalization parameters, Intelligent tutoring systems.