Learning Path Generation Method Based on Migration Between Concepts

The learning strategies often have a direct impact on learning effects. Often, the learning guidance is provided by teachers or experts. With the speed of knowledge renewal going faster and faster, it has been completely unable to meet the needs of the learner due to the limitation of individual time and energy. In order to solve this problem, we propose a learning strategy generation method based on migration between concepts, in which the semantic similarity is creatively applied to measure the relevance of concepts. Moreover, the concept of jump steps is introduced in Wikipedia to measure the difficulty of different learning orders. Based on the hyperlinks in Wikipedia, we build a graph model for the target concepts, and achieve multi-target learning path generation based on the minimum spanning tree algorithm. The test datasets include the books about Computer Science in Wiley database and test sets provided by volunteers. Evaluated by expert scoring and path matching, experimental results show that more than 59% of the 860 single-target learning paths generated by our algorithm are highly recognized by teachers and students. More than 60% of the 500 multi-targets learning paths can match the standard path with 0.7 and above.