A Study On Association Rules Discovery Of Micro-learning Unit Based On Bayesian Network

In recent years,the popularity of mobile Internet and communication devices has brought micro-learning into a new stage of development.The research on micro-learning has also gradually expanded from theoretical research to deeper application aspects and multi-domains.The research on the content of micro-learning resources is also a hot spot among them.Due to its special advantages,the content of micro-learning expands the learning environment and conditions of learners.The streamlined resource content provides convenience for learners to learn,which means they can learn anytime,anywhere.However,due to its streamlined resource content and flexible organization,learners will spend a lot of time on the choice of learning resource content,and because of the learner's own cognitive level and learning habits,it also causes the difference in the content of the selected resource.Learning is a gradual process,from simple to complex,from easy to difficult.Therefore,there is a pre-and post-correlation relationship between resource content,If there is a resource content of the associated relationship,it is recommended that the learner pay attention to the order of learning.The difference in learner's learning progress,the difference in the level of graspingknowledge for certain knowledge points,and the difference in cognitive level will lead to different levels of in mastery of key knowledge.Moreover,the learner's own learning habits are also related to the formation of the learning path.To this end,we propose an association algorithm based on mining the learning resource content in the learner's data set,so as to form a personalized learning path made up of the learning resource content.The traditional association rule algorithm can discover the possible relationship between the micro-learning content from the micro-learning record data according to the support and confidence.However,it is impossible to mine the context before and after the resource content.In view of this,we try to find a method or technology to mine the contextual relationship between resource content,so as to provide learners with personalized micro-learning resource content path.Based on the full study of micro-learning related theories,combined with the content of resources and the characteristics of learners,this paper proposes a Bayesian network association rule algorithm to explore the connection between micro-learning resources.The main work of this paper is as follows:1.The resource content of micro-learning in this paper is mainly studied in two forms: learning behavior and micro-learning unit.The characteristics of learning behavior and micro-learning units are also analyzed.2.According to the requirements of the actual data set of resource content,this paper proposes the concept of learning cycle,which divides thelearner's learning record data set into data sets of different cycles according to the learning cycle.3.Based on this learning cycle,the Bayesian network association rule algorithm is proposed to discover the pre-and post-correlation relationship between resource content,and to form a personalized learning path for the learners.In the end,it helps the learner to effectively complete the choice of resource content and improve the learning effect and efficiency of the learners.