Interactive and dynamic review course composition system utilizing contextual semantic expansion and discrete particle swarm optimization

In the present learning cycle, new knowledge learning and known knowledge review are two important learning processes. Currently, the major attempts of e-Learning systems are devoted to promote the learners’ learning efficiency in new knowledge learning, but only few in known knowledge review. Hence, this paper proposes the review course composition system which adopts the discrete particle swarm optimization to quickly pick the suitable materials, and can be customized in accordance with the learner’s intention. Furthermore, the greed-like materials sequencing approach is also proposed to smoothe the reading order of the course. As a result, such a composition system satisfies the majority of learners with the customized review courses based on their needs.