University of Oulu

Muhterem Dindar, Iman Alikhani, Jonna Malmberg, Sanna Järvelä, Tapio Seppänen, Examining shared monitoring in collaborative learning: A case of a recurrence quantification analysis approach, Computers in Human Behavior, Volume 100, 2019, Pages 335-344, ISSN 0747-5632,

Examining shared monitoring in collaborative learning : a case of a recurrence quantification analysis approach

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Author: Dindar, Muhterem1; Alikhani, Iman1; Malmberg, Jonna1;
Organizations: 1University of Oulu, Faculty of Education, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.9 MB)
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Language: English
Published: Elsevier, 2019
Publish Date: 2021-03-05


The aim of this study is to investigate the relationship between shared monitoring of collaborative learning processes and physiological synchrony between the collaborating group members. Shared monitoring fuels collaborative learning in groups. Video and electrodermal activity data were collected from a group of high school students (two male, one female) during two sessions of collaborative learning. Shared monitoring of learning progress among the group members, in terms of frequency and duration, were coded and calculated in video data. Physiological synchrony in electrodermal activity among the collaborating students was calculated with Multidimensional Recurrence Quantification Analysis (MdRQA). Results revealed that the relationship between physiological synchrony and shared monitoring might be dependent on task type. That is, a significant relationship was observed between MdRQA indices and shared monitoring in one session, whereas no significant relationship was observed in the other session. The current study contributes to the literature on computer-supported collaborative learning through demonstrating the utility of MdRQA to investigate the temporal dynamicity of collaborative learning processes. In conclusion, the chosen methods contribute to research on collaborative learning, as capturing invisible physiological signals and matching them with visible instances of monitoring processes facilitates identification of critical moments in collaboration.

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Series: Computers in human behavior
ISSN: 0747-5632
ISSN-E: 0747-5632
ISSN-L: 0747-5632
Volume: 100
Pages: 335 - 344
DOI: 10.1016/j.chb.2019.03.004
Type of Publication: A1 Journal article – refereed
Field of Science: 516 Educational sciences
113 Computer and information sciences
Funding: This work was supported by the Academy of Finland [SLAM project number: 275440].
Academy of Finland Grant Number: 275440
Detailed Information: 275440 (Academy of Finland Funding decision)
Copyright information: © 2019 Elsevier Ltd. All rights reserved. This manuscript version is made available under the CC-BY-NC-ND 4.0 license