Research on Personalized Learning Path Discovery Based on Differential Evolution Algorithm and Knowledge Graph

Discovering the most adaptive learning path and content is an urgent issue for nowadays e-learning system, to achieve learning goals. The main challenge of building this system is to provide appropriate educational resources for different learners with different interests and background knowledge. The system should be efficient and adaptable. In addition, the best learning path to adapt learners can help reduce cognitive overload and disorientation. This paper proposes a framework for learning path discovery based on differential evolutionary algorithm and Knowledge graph. In the first stage, learners are investigated to form learners’ records according to their cognitive models, knowledge backgrounds, learning interests and abilities. In the second step, learners’ model database is generated, based on the classification of learners’ examination results. In the third stage, the knowledge graph based on disciplinary domain knowledge, is established. The differential evolution algorithm is then introduced as a method in the fourth stage. The framework is applied to learning path discovery based on differential evolution algorithm and disciplinary knowledge graph. The output of the system is a learning path adapted to learner’s needs and learning resource recommendation referring to the learning path.