The optimization of the topology of MOOC educational network based on attraction of the new added courses

Abstract

With the rise ofMassive open online course (MOOC), there are increasing new courses in the education network platforms, which plays a crucial role in the formation and development of MOOC educational network system. However, there are some phenomena of blindly developing MOOC in the practical work. One MOOC educational network is studied as a case, where 9 level-I courses, 46 level-II courses and 230 level-III courses are selected. an improved gravity model is introduced to calculate the attraction between the courses in the MOOC educational network. As network optimization and evolution are interrelated, no optimization exists without evolution. Systemic optimization analysis is necessarily based on the analysis of the topology evolution analysis of the MOOC educational network. The evolution process of MOOC educational network topology, based on the attraction of courses, is a complex dynamic process. There are many rules and mechanisms that have impact on the evolution process. The fundamental theories of complex network models are studied, and a topology evolving model of MOOC educational network is developed with comprehensive consideration of the attraction and node degree of MOOC. Computer simulation of the topology evolution process of the MOOC educational network is carried out applying the proposed model algorithm, and a topology evolution graph of the MOOC educational network based on the attraction of MOOC is formed. Analysis of the network topological evolution supports the research on the topology optimization of MOOC educational network. The basic network parameters of a new network with the same nodes and network structure, but different average degree, may be obtained by changing the code of the evolution model. Furthermore, the optimal average degree of the network topology is selected after analyzing the network parameters of each network. The optimal average degree of the network topology is taken as an average degree of the new MOOCs in a certain MOOC educational network. Network topology parameters of the new MOOCs with the same average degree but different node degree are added in the analysis. The study shows that compared with the initial network, the network topology improves in terms of all basic statistics and the network becomes more effective when the degree of attraction of the new MOOC is set at 100, which shows that the network is optimized. As a result, the new MOOC is selected to join in the MOOC educational network.