Adaptive e-Learning systems are the newest paradigm in modern learning approaches. Nowadays, adaptive e-Learning systems are accountable for selecting learning materials or contents according to the learners' style, profile, interest, previous knowledge level, goal, pedagogical method, etc., all in order to provide highly personalized learning sessions. A number of researches have been conducted in the area of adaptive learning and different adaptive learning methods have been proposed. Due to similarity between learning objects graph and the formalism of Petri Nets, an approach based on Petri Nets for controlling the learning path among learning activities has brought many improvements to the paradigm of adaptive e-Learning systems. A model based on High-Level Petri Nets (HLPN) for modeling and generating behavioral pattern of students has been presented recently, bringing numerous advantages, but also a lot of open issues that need to be resolved. This paper exposes the fundamental characteristics of adaptive e-Learning systems, as well as the students' behavior, such as learning styles and levels of knowledge, thus providing a basis for a novel modeling framework for performance analysis of such systems, as our main goal in future research.
e-Learning systems, Petri Nets, learning style, adaptive learning