Social network learning based on Blue-Red Trees inference and analysis of Rule-Space Model

摘要

It is getting important that applies social network of collaborative learning to intelligent distance learning system. In this paper, we used the Rule-Space Model to infer reasonable Blue-Red trees of learning performance and their definitions. We can derive nine learning groups of social network grouping algorithms and classify particular Blue-Red Trees that belong to specific learning group from previous definitions. They include the general formula for one-to-one complementary collaborative learning group algorithms of strong learning and illustration distribution. Therefore, we use these algorithms to build a social network learning system and social network learning methods with learning performance of Blue-Red trees. Finally, an example for a course with the Rule-Space Model analysis of learning objects is illustrated and proved. From this example, they can be created thirty-six learning performances of Blue-Red trees that are grouped under nine learning groups of social network and inferred one-to-one complementary collaborative learning group algorithms of strong learning. So, the algorithms within the system will recommend those specific Blue-Red trees that satisfy one-to-one complementary collaborative learning group of strong learning.