Quality Improvement of Interactive Tutorial-Like Systems By Use of Cellular Learning Automata

Determining the best way of learning and acquiring knowledge, especially in intelligent tutoring systems have drawn researchers' attention during past years. with regard to studies conducted on e-learning systems and strategies proposed to improve the quality of these systems, it can be said that the interactions play a vital role in the educational systems. therefore, the learners are not only affected by the teacher in a learning environment but also significantly learn important materials through the interaction with other learners. in this article, a new modeling approach is presented for improving learning/teaching models as well as interaction among learners, from which the most benefit can be derived by learners. the proposed model uses cellular learning automata in order to model behavior of the learners as well as interactions between the learners for knowledge acquisition. this algorithm also deals with the process of teaching as well as education of the learners. the results indicate that relationship between the learners can improve their knowledge and also increase their learning speed compared to previous methods.