One-to-One Complementary Collaborative Learning Based on Blue-Red Multi-Trees of Rule-Space Model for MTA Course in Social Network Environment

摘要

It has become increasingly important that applies and develops an intelligent e-learning system in a social network environment. In this paper, we used the combination of Rule-Space Model and multi-tree to infer reasonable learning effects of Blue-Red multi-trees and their definitions through analyzing all learning objects of MTA courses. We can derive one-to-one complementary collaborative learning algorithm from previous definitions. Finally, a MTA course is used to the analysis of Rule-Space Model, and the definition and analysis of learning performance for the MTA Course. From this MTA course, they can create twenty-one learning effects of Blue-Red multi-trees and recommend those specific Blue-Red multi-trees that satisfy one-to-one complementary collaborative learning group algorithm and analyze these learning performances of all Blue-Red multi-trees. They will be the basis of verification for one-to-one complementary collaborative learning.