Research On Personalized Recommendation Method Of Online Learning Resources

At present,massive network learning resources are scattered in the learning platform,and learners cannot quickly find the resources they need and are interested in,causing the problem of "information trekking and overload".To this end,a large number of scholars have begun to study the combination of personalized learning and information recommendation technology,but currently in the field of education,most traditional online learning platforms directly apply mature information recommendation technology,lacking the learner's own personalized feature analysis,serving personality Low degree.How to save the time for learners to retrieve resources in the learning platform,so as to improve learning efficiency and resource utilization has become a hot topic in current research.In response to the above problems,this article mines learner personalized information from multiple angles,and conducts research on personalized recommendation methods for online learning resources.The main research contents are as follows:1.E-learning resources are inseparable from the choice of personalized recommendation technology.In order to effectively manage the correspondence between e-learning resources,organization of core knowledge and test questions,and reasonably obtain effective assessment data for learners,this paper completes the construction of e-learning resource model based on Bloom's education goal classification theory.The model mainly includes drawing a knowledge structure diagram,organizing a learning resource library,and preparing course chapter tests.On this basis,in order to quickly learn the content of different types of learning resources and characterize learner preference information,this paper proposes to represent the characteristics of network learning resources based on tags.2.The learner model is the basis for implementing a personalized recommendation system for learning resources.This article addresses the learning goals and interests of different learners in the field of education.Based on the CELTS-11 learner model specifications,educational goal classification theory,and learning style theory,the learning style data and learning obtained by analyzing the Solomon Learning Style Scale The evaluation data obtained by the learner in the chapter test and the feedback data left by the learner during the learning process,combined with the labeling characteristics of the learning resources,conduct in-depth research on the three personality characteristics of learning style,cognitive level,and interest preferences to complete the learner model build.3.In view of the problem that the information recommendation technology is not highlypersonalized in the application of personalized learning,this paper proposes a personalized recommendation method for network learning resources based on the learner model.This method describes the static and dynamic personality characteristics of learners from different dimensions,combined with the collaborative filtering recommendation method,recommends learning resources that meet the individual needs of learners.After verification,the recommendation method in this paper is superior to the classic collaborative filtering recommendation technology in accuracy,recall and F1 value.And the utilization rate of learning resources has been effectively improved,alleviating the "information trek" problem of learners in the online learning platform.4.Finally,to meet the specific needs of the education field,design and implement a personalized recommendation system for online learning resources.First,the overall architecture and functional modules of the system are designed.Then,by integrating the rich online teaching resources inside and outside the school,with the personalized recommendation function of learning resources as the core,learner information acquisition,course resource learning,learning resource management,and personal resources Management and other functions can provide students with effective learning assistance.