Constructing a personalized e-learning system based on genetic algorithm and case-based reasoning approach

The Internet and the World Wide Web in particular provide a unique platform to connect learners with educational resources. Educational material in hypermedia form in a Web-based educational system makes learning a task-driven process. It motivates learners to explore alternative navigational paths through the domain knowledge and from different resources around the globe. Consequently, many researchers have focused on developing e-learning systems with personalized learning mechanisms to assist on-line Web-based learning and to adaptively provide learning paths. However, although most personalized systems consider learner preferences, interests and browsing behaviors when providing personalized curriculum sequencing services, these systems usually neglect to consider whether learner ability and the difficulty level of the recommended curriculums are matched to each other. Therefore, our proposed approach is based on the evolvement technique through computerized adaptive testing (CAT). Then the genetic algorithm (GA) and case-based reasoning (CBR) are employed to construct an optimal learning path for each learner. This paper makes three critical contributions: (1) it presents a genetic-based curriculum sequencing approach that will generate a personalized curriculum sequencing; (2) it illustrates the case-based reasoning to develop a summative examination or assessment analysis; and (3) it uses empirical research to indicate that the proposed approach can generate the appropriate course materials for learners, based on individual learner requirements, to help them to learn more effectively in a Web-based environment.