Learning behavior mining and decision recommendation based on association rules in interactive learning environment

The interactive learning is a continuous process, which is full of a large number of learning interaction activities. The data generated between learners and learning interaction activities can reflect the online learning behaviors. Through the correlation analysis among learning interaction activities, this paper discusses the potential association rules, defines the data structures, mines the frequent item sets, and designs appropriate algorithms, then recommend learning decision makings based on association rules. The research methods and conclusions can provide feasible educational decision makings for the realization of personalization, probability prediction and decision feedback, which will improve the interactive learning environment, the algorithms, methods and modes designed in this paper are useful supplements for learning analytics.