Graph-based Domain Model for Adaptive Learning Path Recommendation

Adaptive educational hypermedia (AEH) has been an alternative tool to replace conventional learning tools. It mainly offers adaptive navigation and presentation based on a wide range of characteristics, preferences and understanding of learners. The adaptation is enabled by a user model which records users' characteristics, domain models which represent all concepts taught in a course and relationships among them, and goal and adaptation models that infer domain and user models to produce adaptation. The main adaptation offered in AEH deals with where a learner can go next. However, it does not inform learners how far they are from the goals. This paper addresses the problem of finding the shortest path to reach a learning goal. We propose a modified Dijkstra algorithm applied to a graph-based domain model. It takes account of learners' knowledge, the weight of topics in a course and the influence scores of each topic to learn the other topics to predict the possibility of success in learning a topic. Based on the success possibility scores, learning paths are recommended to students. Since students' knowledge is progressive, the success possibility scores are dynamics and they will result in a dynamic learning path adaptive to students' progress.