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

  • Yung-Hui Chen
  • Lawrence Y. Deng
  • Neil Y. Yen
  • Martin M. Weng
  • Bruce C. Kao
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 182)


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.


Rule-Space Model Blue-Red Multi-Tree Social Network Collaborative Learning Complementary Collaborative Learning 


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Copyright information

© Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Yung-Hui Chen
    • 1
  • Lawrence Y. Deng
    • 2
  • Neil Y. Yen
    • 3
  • Martin M. Weng
    • 4
  • Bruce C. Kao
    • 4
  1. 1.Department of Computer Science & Networking EngineeringLunghwa University of Science and TechnologyTaoyuanTaiwan
  2. 2.Department Computer Science and Information EngineeringSt. John’s UniversityTaipeiTaiwan
  3. 3.School of Computer Science & EngineeringThe University of AizuFukushimaJapan
  4. 4.Department of Computer Science & Information EngineeringTamkang UniversityTamsuiTaiwan

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