An Adaptive Method Based on High-Level Petri Nets for E-Learning

Adaptive learning is a new approach for e-learning systems. In comparison to traditional e-learning systems, which present same things for all learners, these systems automatically adapt with learner characteristics. In this paper, we are going to propose a new method for Adaptive learning, and consider adaptation from three viewpoints: 1) learner learning style; 2) learner’s knowledge level; 3) learner’s score. Due to similarity between learning objects graph and petri net, and In order to provide adaptive learning, we use an approach based on a high level petri net (HLPN). Also we propose a method to evaluate performance in this system. We compare our system with a non adaptive system, through our performance evaluating method. The results show response time for our system is less than non adaptive system and learners finish course in a relatively shorter period of time. Since our proposed system considers individual features of learner, we can be sure that learner would not be confused in learning materials.