Predicting Collaboration based on Students' Pauses in Online CSCL Conversations

Sibel Denisleam (Molomer), Mihai Dascalu, Stefan Trausan-Matu

Research output: Contribution to journalArticleAcademicpeer-review

1 Downloads (Pure)

Abstract

As Computer Supported Collaborative Learning (CSCL) gains a broader usage as a viable alternative to traditional educational scenarios, the need for automated tools capable of evaluating active participation and collaboration among peers in online discussions increases. In this study, we validate a quantitative model of predicting involvement in CSCL chats based on student’s pauses throughout the timeline of the conversation. Starting from a corpus of 10 chat conversations, our proposed model explains 55% of the variance in terms of student participation and 42% in terms of collaboration, although relying on simple quantitative indices.
Original languageEnglish
Pages (from-to)83-92
JournalPolytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science
Volume79
Issue number2
Publication statusPublished - Oct 2017
Externally publishedYes

Fingerprint

chat
conversation
participation
learning
student
scenario

Keywords

  • Computer Supported Collaborative Learning
  • pause analysis
  • fluency
  • speed
  • automatic evaluation of participation and collaboration

Cite this

@article{ed406289ef904475b8c520aaa8324a3c,
title = "Predicting Collaboration based on Students' Pauses in Online CSCL Conversations",
abstract = "As Computer Supported Collaborative Learning (CSCL) gains a broader usage as a viable alternative to traditional educational scenarios, the need for automated tools capable of evaluating active participation and collaboration among peers in online discussions increases. In this study, we validate a quantitative model of predicting involvement in CSCL chats based on student’s pauses throughout the timeline of the conversation. Starting from a corpus of 10 chat conversations, our proposed model explains 55{\%} of the variance in terms of student participation and 42{\%} in terms of collaboration, although relying on simple quantitative indices.",
keywords = "Computer Supported Collaborative Learning, pause analysis, fluency, speed, automatic evaluation of participation and collaboration",
author = "{Denisleam (Molomer)}, Sibel and Mihai Dascalu and Stefan Trausan-Matu",
year = "2017",
month = "10",
language = "English",
volume = "79",
pages = "83--92",
journal = "Polytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science",
issn = "2286-3540",
publisher = "Universitatea Politehnica din Bucuresti,Polytechnic University of Bucharest",
number = "2",

}

Predicting Collaboration based on Students' Pauses in Online CSCL Conversations. / Denisleam (Molomer), Sibel; Dascalu, Mihai; Trausan-Matu, Stefan.

In: Polytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science, Vol. 79, No. 2, 10.2017, p. 83-92.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - Predicting Collaboration based on Students' Pauses in Online CSCL Conversations

AU - Denisleam (Molomer), Sibel

AU - Dascalu, Mihai

AU - Trausan-Matu, Stefan

PY - 2017/10

Y1 - 2017/10

N2 - As Computer Supported Collaborative Learning (CSCL) gains a broader usage as a viable alternative to traditional educational scenarios, the need for automated tools capable of evaluating active participation and collaboration among peers in online discussions increases. In this study, we validate a quantitative model of predicting involvement in CSCL chats based on student’s pauses throughout the timeline of the conversation. Starting from a corpus of 10 chat conversations, our proposed model explains 55% of the variance in terms of student participation and 42% in terms of collaboration, although relying on simple quantitative indices.

AB - As Computer Supported Collaborative Learning (CSCL) gains a broader usage as a viable alternative to traditional educational scenarios, the need for automated tools capable of evaluating active participation and collaboration among peers in online discussions increases. In this study, we validate a quantitative model of predicting involvement in CSCL chats based on student’s pauses throughout the timeline of the conversation. Starting from a corpus of 10 chat conversations, our proposed model explains 55% of the variance in terms of student participation and 42% in terms of collaboration, although relying on simple quantitative indices.

KW - Computer Supported Collaborative Learning

KW - pause analysis

KW - fluency

KW - speed

KW - automatic evaluation of participation and collaboration

M3 - Article

VL - 79

SP - 83

EP - 92

JO - Polytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science

JF - Polytechnical University of Bucharest. Scientific Bulletin. Series C: Electrical Engineering and Computer Science

SN - 2286-3540

IS - 2

ER -