Modeling Learner Behavior Analysis Based on Educational Big Data and Dynamic Bayesian Network
Fang Liu, Zimeng Fan, Feihu Huang, Yining Li, Yanxiang He, Wei Hu · 2022
With the deep integration of information technology and education teaching, the learning revolution represented by MOOCs, Khan Academy and flipped classroom is strongly impacting the ecology of traditional education, highlighting the importance of large-scale online education in the process of reshaping education. In the context of large-scale online educational big data, in order to analyze learners' multi-grain feature performance in terms of learning motivation, emotional attitude, cognitive level and social environment, this paper analyzes and extracts scenario-oriented key features of learners by quantifying learning behavior data on the basis of a custom-built real online learning behavior data set, in order to realize the dynamic construction of learners' personalized learning overview and investigate the multidimensional association mapping construction method of generalized resources, build learner-oriented behavior analysis models and perform performance prediction through machine learning dynamic Bayesian networks, and establish better learning path planning after studying the bidirectional influence mechanism between learners and knowledge system.