A Longest Common Subsequence based genetic algorithm for courseware design

Abstract

Personalizing learning is one of the high priority tasks in e-learning. Because of the tremendous importance of Learning Object (LO) in learning process, the attention of many researchers has been attracted to the personalization of LOs. The two main entities in learning are students and educational resources, so the educational activities should give high priority to these entities. In this study, a genetic based algorithm for personalizing the educational slides as an important learning material in traditional and e-learning environments is introduced. Slides and relations between them are showed in form of a graph. The proposed algorithm is applicable to all types of LOs. To evaluate the proposed method, the required data were collected with questionnaires from 36 students that enrolled in Bio Computing course. Analysis of the results shows that the proposed algorithm has been very successful in personalizing the educational slides. It can use to enhance the teaching and learning in traditional and electronic learning environments.