Optimal composition of e-leaming personalization parameters

The combination of personalization parameters for providing personalization of learning scenarios has been an important subject of research in recent years. Several systems have been reported in the literature for assembling the mentioned parameters by reusing existing parameters. However, very little research is available that focuses on optimizing this composition. This work proposes a new approach which allows teachers to choose the optimal composition of personalization parameters by considering the minimal cost of e-learning personalization.