Analysis and Verification of Bisimulation Relationship for Learning Time-Behavior Sequence

Abstract

Similar learner determination has become a research hotspot in the field of personalized recommendation due to its lightweight method. At present, similar learner determination mainly adopts algorithms such as collaborative filtering, but such methods lack interpretability and cannot guarantee the reliability of the determination results. Aiming at this problem, this paper proposes a method for determining and verifying the similarity of learning time behavior sequences based on bisimulation, which uses logical reasoning to determine the similarity of learners, and uses tools to verify the correctness of the determination results. Firstly, the behavior properties of Temporal Calculus of Communication System (TCCS) are extended, and Learning Resources-Temporal Calculus of Communication System (LR-TCCS) is proposed to model the learning time-behavior sequence of learners. Secondly, we determine the similarity of learning time-behavior sequence through bisimulation. Thirdly, the bisimulation verification tool MWB is used to verify the similarity of learning time-behavior sequence, and to determine the optimal learner among similar learners through time allocation algorithm and behavior frequency analysis algorithm; Finally, the effectiveness of the method is verified by an example.