Improving group selection and assessment in an asynchronous collaborative writing application

Nobel Khandaker, Leen-Kiat Soh

Research output: Contribution to journalArticle

5 Citations (Scopus)

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

Fingerprint

Students
Group
student
group membership
teacher
learning
Learning systems
efficiency

Keywords

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

ASJC Scopus subject areas

  • Education
  • Computational Theory and Mathematics

Cite this

Improving group selection and assessment in an asynchronous collaborative writing application. / Khandaker, Nobel; Soh, Leen-Kiat.

In: International Journal of Artificial Intelligence in Education, Vol. 20, No. 3, 01.12.2010, p. 231-268.

Research output: Contribution to journalArticle

@article{f6210a4b681c478b8b293909a10d4582,
title = "Improving group selection and assessment in an asynchronous collaborative writing application",
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.",
keywords = "Computer-Supported Collaborative Learning, Group Formation, Multiagent System",
author = "Nobel Khandaker and Leen-Kiat Soh",
year = "2010",
month = "12",
day = "1",
doi = "10.3233/JAI-2010-0008",
language = "English (US)",
volume = "20",
pages = "231--268",
journal = "International Journal of Artificial Intelligence in Education",
issn = "1560-4292",
publisher = "IOS Press",
number = "3",

}

TY - JOUR

T1 - Improving group selection and assessment in an asynchronous collaborative writing application

AU - Khandaker, Nobel

AU - Soh, Leen-Kiat

PY - 2010/12/1

Y1 - 2010/12/1

N2 - 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.

AB - 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.

KW - Computer-Supported Collaborative Learning

KW - Group Formation

KW - Multiagent System

UR - http://www.scopus.com/inward/record.url?scp=79951891311&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79951891311&partnerID=8YFLogxK

U2 - 10.3233/JAI-2010-0008

DO - 10.3233/JAI-2010-0008

M3 - Article

AN - SCOPUS:79951891311

VL - 20

SP - 231

EP - 268

JO - International Journal of Artificial Intelligence in Education

JF - International Journal of Artificial Intelligence in Education

SN - 1560-4292

IS - 3

ER -