Personalized Learning Path Generation Scheme Utilizing Genetic Algorithm for Web-based Learning

[[abstract]]Curriculum sequencing is an important research issue for Web-based learning systems because no fixed learning paths will be appropriate for all learners. Therefore, many researchers focused on developing e-learning systems with personalized learning mechanisms to assist on-line Web-based learning and adaptively provide learning paths. However, although most personalized systems consider learner preferences, interests and browsing behaviors in providing personalized curriculum sequencing services, these systems usually neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning processes, thus reducing learning performance. Besides, the problem of concept continuity of learning paths also needs to be considered while implementing personalized courseware generation because smoother learning paths increase learning performance, avoiding unnecessarily difficult concepts. Therefore, this paper presents a genetic-based curriculum sequencing approach to generate personalized courseware through simultaneously considering courseware difficulty level and the concept continuity of learning paths for Web-based learning. In the proposed method, a pretest through the computerized adaptive testing (CAT) is first applied to collect the incorrect learning concepts of learners. After that, the difficulty parameter of courseware and the concept relation degrees of courseware are simultaneously considered to determine the fitness function of genetic algorithm in order to generate personalized learning path for individual learner. Experiment results indicate that applying the proposed genetic-based curriculum sequencing approach for Web-based learning can generate high quality learning paths, and help learners to learn more effectively.