Research And Application Of Learning Resource Recommendation Method Based On Artificial Raindrop Algorithm

With the continuous deepening of education informatization,the development of wisdom education has entered a new stage in China.Smarter learning environment have changed the way of teaching and learning.Under the background,online educational resources are growing explosively,which causing the problems of resource "information overload" and learners"knowledge trek".In view of this situation,according to the individual needs and the behavior records generated by learners on the learning platform,this thesis studies a learning resource recommendation method based on artificial raindrop.And an adaptive learning recommendation system is designed and implemented.The specific research,contents are as follows:Firstly,due to the independent existence of knowledge points,it is difficult to associate knowledge points with learners’ cognitive structure.This thesis uses the mathematics knowledge of the People"s Education Edition junior high school as the research object.According to the knowledge point hierarchy,a top-down approach is used to manually construct the junior high school mathematics knowledge graph,which can be applied to the learning task guidance later.Knowledge graph connect know-ledge poin,ts and their relationships in the form of a network structure.The successful construction of the graph can not only strengthen the connection between mathematical chapters and help learners to store them in a structured way,but also use the connections between knowledge points to effectively locate and track learners,thereby helping learners to learn more systematically.Considering the high labor cost and the expansion of the knowledge graph in the later stage,this thesis uses a semi-automatic method to complete the knowledge extraction and knowledge fusion work with the support of deep learning.At the same time,the graph database is used to store and visualize the knowledge graph.Secondly,for the lack of personalization and applicability of the recommendation results of the current learning resource recommendation method,this thesis designs a learning resource recommendation method based on the mapping relationship.And under the support of the standard learner model,this thesis focuses on three personalized indicators of learners’ learning goals,learning styles and cognitive level.Learning objectives represent the ideal state of mastery of a knowledge point by learners in the early stage of learning.Learning styles describe the user’s preferred learning patterns.Cognition is the embodiment of the learner’s ability to learn.After the collecting and processing of the learner’s historical behavior data,this thesis collects learning behaviors such as the browsing time,downloads and collections of learning resources.According to factors such as the matching degree between learning style and resource type information characteristics,time costs and investment in the learning process,this thesis calculates personalized indicators and builds a learning resource recommendation model.Finally,in this thesis,the differential artificial raindrop algorithm based on the perturbation mechanism is used to realize the optimization of personalized learning resources.The recommendation problem is essentially a combinatorial optimization problem.Therefore,based on disturbance mechanism,this thesis adopts a differential artificial raindrop algorithm to achieve the recommendation purpose.In order to increase the diversity of the population,parameter is used to adjust the collision operator in the raindrop collision process.And by introducing a perturbed difference strategy into the flow operator,the local search and global search capabilities of the algorithm are balanced.The experimental results show that the artificial raindrop algorithm has better performance in dealing with discrete combinatorial optimization problems.The improved artificial raindrop algorithm has faster convergence speed and higher solution accuracy.The personalized learning resources recommended in this thesis are more interpretable.Based on the above research methods,this thesis designs and implements an adaptive learning recommendation system.According to the concrete realization of the functions of system test question test,and learning resource guidance.The rationality,feasibility and effectiveness of the resource recommendation model are verified.The learning resource recommendation model studied provides a new reference for the subsequent learning effect evaluation.