Pedagogically-driven courseware generation for intelligent e-learning systems

Abstract

The thesis presents a novel approach to adaptive courseware generation, named PCG. This approach adopts its structure from existing intelligent e-learning systems and introduces a new component called pedagogical model to support pedagogical flexibility in the process of courseware generation. The process is carried out in three steps. The first step allows the selection of a pedagogic scenario that better matches to the learner’s characteristics. The second step allows the generation of a courseware plan whose structure is featured by the selected pedagogic scenario, while the third step enables the generation of adaptive courseware content by substantiating the courseware plan with appropriate resources. The generated courseware is a sound courseware satisfying the design constraints imposed by the learner, knowledge domain, and pedagogical models. PCG prototype was experimentally evaluated using user study. The overall results of the experiment study demonstrated the satisfaction of learners with the system and proved a significant improvement in the learning gain.