Network Visual Exploration for the Cooperation Map of Courses in Different Major Curricula

As the data science rising massive changes in many fields, effective visualization is urged for the huge amounts of data, also in the education industry is no exception. Education contains a great quantity of linked data whose key value lies in the connection. However, we do not know how different courses can carry out diversified cooperation in different major curricula. This paper proposes a network modelling approach to curriculum mapping depend on the 11 years' data of a university curriculum in the United States, using the network structure to study the curriculum connection from a network dynamic perspective, the network models present a visualize pattern with identify relationships and attributes around different courses. We also provide various descriptive statistics analysis data, such as density, average clustering, and number of nodes etc. Using complex network mapping the curriculum pattern of university education provide a gap analysis of a visualization pattern of educational curriculum research.