Research On Recommended Methods And Application Of Mobile Learning Resources For Secondary Vocational School Students

With the deep integration of information technology and educational applications,mobile learning has become one of the important ways of online learning.Because mobile learning can effectively integrate theoretical learning content with physical scene knowledge,it is in line with the goal of vocational school professional course theory teaching and post ability knowledge training.Mobile learning has become a hot topic in vocational school teaching application research.However,in the face of a large number of learning resources in the mobile learning platform,learners are prone to resource screening difficulties and cognitive load aggravation.At the same time,the existing resource recommendation model has insufficient analysis of the characteristics of secondary vocational students and the characteristics of learning resources,The matching between the learning resources pushed by the existing mobile learning platform and the students' demand resources is low,and the recommendation accuracy is not high.In order to solve the above problems,this paper proposes a mobile learning resource recommendation method(edMOPSO-RA)for secondary vocational students.Firstly,it sorts out domestic and foreign literatures on learning resource recommendation,mobile learning resource recommendation,mobile learning resource recommendation technology and situational factors.Secondly,it analyzes location context,network context,time context factor and secondary vocational students are characterized by emotional instability and lack of interest in learning,combined with the difficulty,type,size and time characteristics of learning resources,a two-dimensional recommendation model is constructed,which integrates the learner's situational features and the learning resources' s contextual features,which is the mobile learning resource recommendation model for secondary vocational students.FSSM);Again,according to the low accuracy of recommended resources and the homogenization of recommended resources in the recommendation process of mobile learning resources,a multi-objective particle swarm optimization algorithm based on evolutionary state detection(edMOPSO)is designed;by using the convergence performance and diversity of the evolutionary information detection algorithm to reflect the accuracy state and homogenization of the recommended resources,and then choose different optimization strategies to improve the accuracy of resource recommendation.Finally,the proposed algorithm edMOPSO is used to optimize the FSSM model,and a mobile learning resource recommendation method for secondary vocational students is proposed.In this paper,the validity of the mobile learning resource recommendation algorithm is verified by the benchmark function.The operational efficiency and feasibility of the proposed edMOPSO-RA method are verified by the performance analysis of the mobile learning resource recommendation method and the feasibility analysis of the application.The practical application process of the proposed method is illustrated by the chapter "Evergreen Plants" in the "Plant Recognition" course of the secondary vocational school.The above experiments show that the proposed method can improve the matching of recommended resources with the demand resources of secondary vocational students.and help to improve the accuracy of resource recommendation.