Personalized learning path generation scheme utilizing genetic algorithm for web-based learning

Personalized 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 in order to promote the learning performance of individual learners. However, most personalized e-learning systems usually neglect to consider if learner ability and the difficulty level of the recommended courseware are matched to each other while performing personalized learning services. Moreover, the problem of concept continuity of learning paths also needs to be considered while implementing personalized curriculum sequencing because smoother learning paths increase learning performance, avoiding unnecessarily difficult concepts. Generally, inappropriate courseware leads to learner cognitive overload or disorientation during learning processes, thus reducing learning performance. Therefore, this paper presents a prototype of genetic-based personalized e-learning system which can generate appropriate learning paths according to the incorrect testing responses of individual learners in a pre-test. Based on the results of pre-test, the proposed genetic-based personalized e-learning system can perform personalized curriculum sequencing through simultaneously considering courseware difficulty level and the concept continuity of learning paths to support Web-based learning. Experiment results indicate that applying the proposed genetic-based personalized e-learning system for Web-based learning can generate high quality learning paths, and help learners to learn more effectively.