Research On Key Technologies Of Personalized Learning For Farmers Modern Distance Education

The implementation of modern distance learning for farmers is an important strategic plan to improve the level of rural information, eliminate the digital gap, and construct the system of lifelong learning. At present, the growth speed of online learning resources is rapid with development of computer and communication technology. Farmers in distance education learning process encounter the problems of information loss and resource overload, the main reason is that the distance learning system can’t effectively understanding of teaching resources semantic information, different structure of teaching resources can’t effectively organized into useful knowledge at present. In addition, the system can’t provide personalized learning services for users according to different learning needs and backgrounds. How to quickly and effectively obtain the personalized learning information from a large number of learning resources has become an urgent need for users.The personalized learning key technologies in modern distance learning for farmers are mainly discussed in the paper, and the methods of theoretical modeling, mathematical analysis, experimental simulation and actual deployment are used. There are some achievements in the theoretical research and practical exploration of personalized learning key technology in the paper:(1) The personalized system framework for farmers’ distance learning is proposed. According to distance teaching resources, farmers learning behavior and teaching resource attribute information, with computer and information technology as support, the paper proposes a high performance of personalized system framework for farmers’ distance learning.(2) The domain ontology of distance learning video teaching resources for farmers is developed and constructed. The construction method of domain ontology is researched and improved. The domain ontology of distance learning video teaching resources for farmers can provide reliable semantic knowledge support for user interest model construction.(3) A personalized user interest model based on user attribute information and teaching resources domain ontology is researched and constructed for the prediction of user behavior changes is inaccurate, algorithm space complexity is high, the analysis of the user learning behavior impact is not accurate and comprehensive.(4) The collaborative filtering recommendation algorithm based on the combined similarity of user attributes and interest topics is proposed in order to solve the problem of cold start and sparse matrix in personalized recommendation. The personalized recommendation algorithm based on sequence analysis is researched, and the experiment result shows that the minimum support threshold should set in the range between 0.003% to 0.004%, and then the accuracy and coverage of the personalized recommendation exhibits better.(5) Farmers Distance Personalized Learning System(FDPLS) prototype is researched and developed. In practice, the key technologies and methods of domain ontology of distance learning video teaching resources, user interest model and personalized recommendation algorithm are proved effective.The main innovation points are summarized as follows: the personalized user interest model based on user attribute information and teaching resources domain ontology is proposed,which is basis on research and construction of domain ontology of distance learning video teaching resources, and the problem of user interest model lack of hierarchy concept semantic is solved. At the same time, the user interest model has good expansibility and self adjustment ability. The collaborative filtering recommendation algorithm based on the combined similarity of user attributes and interest topics is proposed, which is basis on research of domain ontology and user interest model. The problems of user score sparse matrix and high time complexity of personalized recommendation algorithm are alleviated.