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

Fatemeh Omrani, Ali Harounabadi, Vahid Rafe

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DOI: 10.4236/jsea.2011.410065                

Abstract

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.

Keywords

Adaptive Learning, Learning Style, High Level Petri Nets, Response Time

Conflicts of Interest

The authors declare no conflicts of interest.

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