Multi-turn Dialogue Tutoring Model Based on Knowledge Forest

In the intelligent tutoring scenario,the intelligent tutoring algorithm of traditional knowledge graph cannot represent the cognitive relationship between knowledge topics,and the traditional recommendation method lacks the dynamic interaction design between students and the knowledge system.These two problems will lead to students being lost in the learning process.Multi-turn dialogue tutoring model based on knowledge forest is from the perspective of the knowledge forest model.This paper proposes a tutoring model that covers the sequence structure of knowledge,knowledge centrality and difficulty,and dynamic interactive.It quantitatively evaluates the cost of knowledge lost faced by students in the learning process around the centrality and prior knowledge.In order to verify the effectiveness of the algorithm,this paper quantitatively analyzes the knowledge disorientation problem of random learning strategy,and carries out comparative experiments between multi-turn dialogue tutoring model and random learning strategy on subject datasets GeoQSP,HisQSP,PhyQSP and DSAQSP.The experimental results show that the multi-turn dialogue tutoring model based on knowledge forest can greatly alleviate the problem of knowledge lost caused by the lack of cognitive relationship between knowledge topics and the lack of dynamic interaction ability.