Building A System For Personalized Learning Plan

Abstract

Nowadays, personalized learning becomes more attractive and worth the investment to educators. There are many studies on this field and many solutions have been proposed. All of these solutions can be considered as selection for each specific background. Therefore, in this paper, we proposed a method based on graph theory to design personalized learning curriculum supporting or combining many different strategies but not violating the compulsory requirements. Thanks to principles and logical conditions, this method maintains the absolute accuracy of the results. First, it includes an abstract graph to receive the input information and an exploration graph stores all the possible paths of the curriculum. Second, our exploration graph represents all possible learning paths, and then, we apply different strategies to select the most suitable paths for our learning target. Moreover, this method allows users to add personal conditions or personal strategy to customize their personal learning plan