Nguyen, A., Järvelä, S., Rosé, C.,Järvenoja, H., & Malmberg, J. (2023). Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics. British Journal of Educational Technology, 54, 293–312. https://doi.org/10.1111/bjet.13280312
Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics
|Author:||Nguyen, Andy1; Järvelä, Sanna1; Rosé, Carolyn2;|
1Learning & Educational Technology Research Unit (LET), Faculty of Education, University of Oulu, Oulu, Finland
2Language Technologies Institute, Human-Computer Interaction Institute, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
|Online Access:||PDF Full Text (PDF, 1.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023032933686
John Wiley & Sons,
|Publish Date:|| 2023-03-29
Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace-based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace-based approach to study SSRL, there remains a paucity of evidence on how trace-based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students’ physiological activities and their SSRL interactions to provide trace-based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace-based data could be utilised to predict learning outcomes in collaborative learning.
British journal of educational technology
|Pages:||293 - 312|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
516 Educational sciences
113 Computer and information sciences
This work was funded in part by Finnish Academy projects nos. 32438 and 350249; and by National Science Foundation grant no. 1917955.
|Academy of Finland Grant Number:||
350249 (Academy of Finland Funding decision)
© 2022 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.