Graph-based domain model for adaptive learning path recommendation
Muhammad Fiqri, Dade Nurjanah · 2017
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.