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.
Highlights
In e-learning systems, learners are faced with considerable amount of information in different format
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)
Summary
In e-learning systems, learners are faced with considerable amount of information in different format. Because the presented learning material which is available for learner, due to content, is adaptive with user’s characteristics; and due to background knowledge level and his/her score, the path which he/she travels, can be different. Learner improvement is assessed by checking leaner’s score Checking this factor increases learner’s performance and arriving to his/her learning target in shorter time. De Marcos et al proposed a PSO algorithm for presenting adaptive link to user [5] In this method, sequencing problem of the learning object for learner, is resembled to permutCSP problem and to solve this problem; used an agent which. We should add that none of the previous methods based on Petri net, such as [2,3], use learning style for adaptation; whereas this is a key factor in learner satisfaction.
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