Improving group selection and assessment in an asynchronous collaborative writing application

Nobel Khandaker, Leen Kiat Soh

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Two critical issues of the typical computer-supported collaborative learning (CSCL) systems are inappropriate selection of student groups and inaccurate assessment of individual contributions of the group members. Inappropriate selection of student groups often leads to ineffective and inefficient collaboration, while inaccurate assessment of individual contributions of the group members (1) hinders healthy working relationships among members and (2) prevents teachers from providing precise interventions to specific students. To address these issues, our proposed iHUCOFS framework forms student groups by balancing the students' competence (what the students know) and compatibility (whom they like as peers) for each group. The competence and compatibility are calculated using the assessment of student contributions derived from a newly implemented asynchronous collaborative writing module's detailed tracking information. Results suggest that: (1) the use iHUCOFS framework may improve: (a) the effectiveness and efficiency of the groups, (b) the perception of the students of their peers and their groups, and (c) the collaboration among students with low and high competence and (2) the teacher can use the detailed information tracked by the collaborative writing module to: (a) improve the design of the CSCL tools and (b) provide precise intervention to improve collaboration among the students.

Original languageEnglish (US)
Pages (from-to)231-268
Number of pages38
JournalInternational Journal of Artificial Intelligence in Education
Volume20
Issue number3
DOIs
StatePublished - Dec 1 2010

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Keywords

  • Computer-Supported Collaborative Learning
  • Group Formation
  • Multiagent System

ASJC Scopus subject areas

  • Education
  • Computational Theory and Mathematics

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