Random Field Design and Collaborative Inference Strategies for Learning Interaction Activities

Learning interaction activities are the key part of tracking and evaluating learning behaviors, that plays an important role in data-driven autonomous learning and optimized learning in interactive learning environments. In this study, a big data set of learning behaviors with multiple learning periods is selected. According to the instance characteristics of data attributes and learning behaviors, the analysis of learning interaction activities and their relationships is mapped to the optimized design of Markov random fields. Appropriate models and multi-level algorithms are constructed to demonstrate the maximal cliques that affect the learning behaviors and mine the critical paths, then cooperative inference strategies of learning interaction activities are deduced. In order to verify the effectiveness of the random fields and analysis technology of learning interaction activities, several approximate algorithms are introduced to fully compare multiple statistical indexes. To verify the feasibility of collaborative recommendation strategies, the improvement effect of learning behaviors and learning effect is tested by applying it to the actual teaching process. Research and analysis show that the random fields of learning interaction activities and the collaborative inference strategies have advantages in data analysis, topological relationship construction and teaching practice guidance.