Järvelä, S., Nguyen, A., Vuorenmaa, E., Malmberg, J., & Järvenoja, H. (2023). Predicting regulatory activities for socially shared regulation to optimize collaborative learning. In Computers in Human Behavior (Vol. 144, p. 107737). Elsevier BV. https://doi.org/10.1016/j.chb.2023.107737.
Predicting regulatory activities for socially shared regulation to optimize collaborative learning
|Author:||Järvelä, Sanna1; Nguyen, Andy1; Vuorenmaa, Eija1;|
1Learning & Educational Technology Research Lab (LET), Department of Educational Sciences, P.O.BOX 2000, 90014, University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe20231103142886
|Publish Date:|| 2023-11-03
This study utilized multimodal learning analytics and AI-based methods to examine the patterns of the socially shared regulation of collaborative learning (CL). The study involved multimodal data involving video and electrodermal activities (EDA) data collected from ninety-four secondary school students (N = 94) during five science lessons to reveal trigger events in CL. A novel concept of trigger events is introduced, which are challenging events and/or situations that may inhibit collaboration and will, therefore, require strategic adaptation in the regulation of cognition, motivation, and emotion within the group. The ANOVA results for the Skin Conductance Responses (SCRs) analysis indicated the disparity of physiological behavior activated in relation to different types of interactions for regulation. Process analysis and episode-rule mining were applied to reveal regulatory patterns in CL, while an AI approach with long short-term memory (LSTM) deep-learning networks were designed for pattern prediction. LSTM has emerged as the most widely applied artificial recurrent neural network (RNN) architecture for sequential data analysis and classification. The proposed AI network holds the potential for designing solutions for similar signal-processing problems in studying learning regulation. This study contributes to developing AI-enabled real-time support for regulation in collaborative learning.
Computers in human behavior
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
516 Educational sciences
113 Computer and information sciences
This research was granted by Finnish Academy Grant No. 324381 and 350249 and Profi 7 352788.
|Academy of Finland Grant Number:||
324381 (Academy of Finland Funding decision)
350249 (Academy of Finland Funding decision)
The authors do not have permission to share data.
© 2023 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).