The Design And Implementation Of Personalized Recommendation Technology In The Happy Learning Platform

Education can change the fate of a person.To some extent,the level of education can affect the overall level of civilization of a country.With the development of technology and society,the form of education will no longer stay in the traditional form of school education.For the promotion of the development of the education course,the rise of the online education has brought challenges and opportunities.Through the use of other online assisted learning system,we found that students have to spend more and more time to search the resources they need with the increasing number of the learning resources.It is not convenient but increasing the trouble for the students to find the right resources,which even seriously reduces the efficiency of learning and makes students abandon the use of the online learning resources system at last.Therefore,it is an inevitable trend to create personalized learning resource recommendation system for the users in order to improve their satisfaction when they are using the online learning resource system.In this paper,we design and realize a happy study recommendation platform which can supply a kind of learning resourse named Courseware which reflect the thinking of the flipped classroom teaching mode and grasp the psychological characteristics of the primary and middle school students closely.The design of Courseware can lead the students to change them from passive learning to active learning and cultivate their self-educated abilities as well as prepare for the daily classroom teaching.Firstly,we researched the popular personalized recommendation technology,Although collaborative filtering recommendation technology has a cold start and data data sparseness,the collaborative filtering recommendation technology has the characteristics of the high degree of automation recommendation,no professional knowledge and the ability to discover new interests of users,Therefore,we finally choose the collaborative filtering technology to recommend learning resources.At the same time,other recommendation techniques are also incorporated.Secondly,among the learning resources provided by the happy study platform,Courseware is the one of the most important learning resources.According to the characteristics of primary and middle school students' short attention time and low self-study ability,one chapter is made as a Courseware which includes 3 to 5 knowledge points.Each knowledge point contains a less than or equal to 15 minutes teaching video and a pass test which includes 3 to 5 objective choice questions.At the end of the Courseware there is also a final exam which has 15(at most)objective questions.The platform can record the student's learning behavior of each knowledge point.Thus,the real learning situation of each student can be obtained by the platform.Then,according to the preset score table,the platform can actively recommend other related learning resources to the student.Based on the characteristics of the Courseware,we optimize the existing collaborative filtering algorithm.Finally,based on the detailed analysis of personalized recommendation of online learning resources,this paper focuses on the personalized recommendation process and resource recommendation module.The optimized collaborative filtering algorithm is applied to the resource recommendation module of the happy study platform,which effectively meets the students' demand for the personalized recommendation services.At the same time,other important function modules of the platform are also elaborated,such as:student management module,resource search module,resource learning module,resource management module,order management module and platform management module.