The best learning order inference based on blue-red trees of rule-space model for social network

Network learning is becoming increasingly popular today. It is getting important to develop adaptive learning by social network that can be applied in intelligent e-learning systems, and provide learners with efficient learning paths and learning orders for learning objects. Therefore, we use the rule-space model to infer reasonable learning effects of blue-red trees and their definitions through analysing all learning objects of courses within system. We can also define all part learning of sub-binary trees from a course and derive all learning paths from each part learning of sub-binary tree based on the premise that we had inferred nine learning groups of social network grouping algorithms. Most importantly, we can define the relation weight of every learning object associated with the other learning objects, and separately calculate the confidence level values of between two adjacent learning objects from all learning paths. And finally, we can find the optimal learning orders among all learning paths from a sub-binary tree in the case of ITE course.