Three Common Group Formations in Online Collaborative Learning

Tao Wu, Maiga Chang

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

1 Citation (Scopus)


With the widespread use of emerging computer technologies in teaching and learning, computer-supported collaboration as a beneficial teaching and learning strategy is now embraced in campus. Analyzing the influencing factors of individual participation is important to discover a more efficient group forming in terms of accomplishing collaborative learning tasks. Through the social network analysis approach on the collected data, this research has found that personality trait does affect a student’s activeness and the group’s mode. Three main group modes are discovered: Unipolar, Multi-Center, and Centerless-Flat mode. Multi-Center group mode is more stable and has higher average academic achievement than the other two modes. The research findings can be used to implement an intelligent tutoring system that can make recommendations for teachers on the better options of grouping students based on their personality traits.

Original languageEnglish
Title of host publicationIntelligent Tutoring Systems - 17th International Conference, ITS 2021, Proceedings
EditorsAlexandra I. Cristea, Christos Troussas
Number of pages7
Publication statusPublished - 2021
Event17th International Conference on Intelligent Tutoring Systems, ITS 2021 - Virtual, Online
Duration: 7 Jun. 202111 Jun. 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12677 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Conference on Intelligent Tutoring Systems, ITS 2021
CityVirtual, Online


  • Big Five Inventory (BFI)
  • Collaborative Learning
  • Personality traits
  • Social messaging app
  • Social network analysis


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