Research And Application Of Online Learning Platform Based On Personalized Learning Path Recommendation

The emergence of a series of online learning platforms,such as the National Smart Education Platform,demonstrates the accelerating transformation of education informatization.With the emergence of online learning platforms,learners can access the knowledge they need anytime and anywhere on the Internet.However,compared with traditional classrooms,online learning platforms ignore the differences between learners and learning resources,and the wide range of learning knowledge points and many types of content resources make it easy for learners to get lost in the process of mastering knowledge,thus failing to recommend learning paths that meet their characteristic needs.To address the above problems,this paper analyzes the personalized learning path recommendation problem in terms of learner and learning resource characteristics,and optimizes the personalized learning path recommendation model by improving the binary particle swarm algorithm to improve the accuracy of personalized learning path recommendation.The research work of this paper mainly includes:1.A nonlinear factorial stickiness binary particle swarm optimization(NFSBPSO)is proposed.The NFSBPSO algorithm is improved in terms of population initialization and the decreasing strategy of the viscous factor.The population initialization adopts a logistic chaos strategy and the stickiness factor adopts a nonlinear decreasing strategy,which enhances the diversity of particles and the NFSBPSO algorithm in terms of global and The exploration ability between global and local is better balanced.2.A learning path recommendation algorithm based on two-dimensional feature model(TDFM-LP)is proposed.By analyzing the characteristics of learners and learning resources in the online learning platform,a learning path recommendation model based on two-dimensional features of learners and learning resources is constructed,and the NFSBPSO algorithm is applied to the model,which is optimized to obtain an optimal solution of the learning path.Experiments show that the algorithm can obtain a learning path with a high matching degree.In order to verify the effectiveness and practicality of the proposed algorithm in the learning path recommendation problem,it is applied to the developed online learning platform to show learners the learning paths and learning resources that meet their needs.Compared with the existing online learning platform,the learning efficiency of learners and the accuracy of learning path recommendation are improved.