Research On Learning Resources Recommendation Strategy Based On Knowledge Relevance And System Implementation

With the rapid development of electronic learning,and the education big data era has arrived.In various types of digital learning platforms,the quantity of digital learning resources explosive growth.Learners can acquire plentiful learning resources,at the same time,the problem of information overload has become increasingly serious.Learners easily face the problem such as knowledge navigation loss and learning subject drift.And the personalized recommendation technology for learning resources is an effective means to solve such problems.At present,research on the personalized recommendation of learning resources,has used personalized recommendation technology to solve the above questions to a certain extent.But,it lacks the grasp of knowledge relevance among learners,knowledge points,and learning resources,also has a flaw in consolidating,correcting and predicting learners' learning paths.Therefore,with the deepening of learning,the learning is incoherent and systematic.Finally,the problem of knowledge navigation loss,theme drift,and learning interest decline appears.To solve these problems,this paper proposes a recommendation strategy for learning resources based on knowledge relevance,and designs and implements a personalized recommendation system by this recommendation strategy.The main work includes following parts.Firstly,this paper uses the relationship between knowledge and resources to analyze the knowledge relevance among learners,knowledge points and learning resources,and built the association model of learner's knowledge resource.Meanwhile,using this model to analyze learners' learning paths and learning styles.Secondly,combining the association model of learner's knowledge resource with personalized recommendation technology,a learning resources personalized recommendation strategy was proposed,which including content-based recommendation algorithm and collaborative filtering recommendation algorithm based on knowledge relevance.This strategy explores learners' interest,analyzes learning path,ensures the continuity and systematization of learning,by learner's knowledge relevance model and knowledge similar users' cluster.Finally,based on the above recommendation strategy,a personalized recommendation system for learning resources based on knowledge relevance has entered into resource convergence platform.And,this thesis devises the system framework of client,server and database.Three main functional modules are completed,including resource recommendation module,resource interpretation module and the data collection module of learning behavior.Furthermore,functional testing and performance testing of the system have verified the effectiveness and stability of the system in many aspects.