A personalized self-learning system based on knowledge graph and differential evolution algorithm

Discovering the most adaptive learning path and content is an urgent issue for nowadays e-learning environment, for achieving learning goals efficiently and effectively. The main challenge of building this system is to provide appropriate educational guide and resource for different learners with respective interests and knowledge base. In order to reduce people's cognitive overload and fulfill their self-learning requirements, this article proposes a framework for a self-learning system. The system is design to be closed and updated automatically, in which learning path is discovered based on differential evolution (DE) algorithm and knowledge graph. The output of the system includes: (1) the personalized learning path adapted to learner's specific needs; (2) learning resource recommendation matching the learning path; (3) test results of learners' learning effect after following the learning path and resources recommendation; (4) revised learning path and resources recommendation according to learner's evaluation. Experimental results show that the system based on DE algorithm and disciplinary knowledge graph is feasible in optimal learning path discovery and further learning resources recommendation.