Design And Implementation Of Data-driven Learning Resource Precise Recommendation System

In the context of the era of educational big data,personalized adaptive learning has become the new normal of digital learning.The way learners obtain learning resources has changed from "active learner search" to "learning systems automatically provide learners with personalized learning resource services." It is an inherent requirement of personalized adaptive learning.The development and application of educational big data provides a scientific basis for achieving personalized and precise learning support services.Learning resource recommendation is an important application direc tion of personalized adaptive learning system.It was originally used to solve the problems of "information overload" and "information trek" caused by massive learning resources.However,the amount of data analyzed by the traditional learning resource recommendation system is small.The design of learning resource recommendation algorithms is not in place,which has become the main reason for hindering the improvement of learning resource recommendation effects.In response to the above problems,this paper uses education big data to more scientifically and comprehensively mine,store,analyze and apply the data generated by each learner.At the same time,by using a hybrid recommendation method that uses multiple recommendation algorithms to recommend in parallel,it avoids the past on the basis that the recommendation algorithm configuration is not in place,give full play to the technical advantages of educational big data and improve the effectiveness and accuracy of the personalized learning resource recommendation service.Based on the relevant learning theory,the author from the system architecture design,system model design,recommendation algorithm Design the data-driven learning resource accurate recommendation system model in terms of design and so on.Taking the "C language programming" course as an example,the system database of the recommendation system is constructed from three aspects: user database,course resource database and learning behavior database.Finally,in Driven by the recommendation system database,it implements a variety of recommendation methods and parallel recommendation of parallel learning resources.Practice has shown that by properly configuring the recommendation algorithm and establishing a complete database of recommendation systems,data-driven learning resource recommendation services can be better achieved.Subsequent research needs to correlate larger data to provide users with more scientific and accurate learning Resource recommendation service to support learners' personalized adaptive learning.