Learners’ Linguistic Alignment and Physiological Synchrony: Identifying Trigger Events that Invite Socially Shared Regulation of Learning
Lämsä, Joni; Edwards, Justin; Haataja, Eetu; Sobocinski, Marta; Peña, Paola R.; Nguyen, Andy; Järvelä, Sanna (2024-07-25)
Lämsä, Joni
Edwards, Justin
Haataja, Eetu
Sobocinski, Marta
Peña, Paola R.
Nguyen, Andy
Järvelä, Sanna
Society for Learning Analytics Research
25.07.2024
Lämsä, J., Edwards, J., Haataja, E., Sobocinski, M., Peña, P., Nguyen, A., & Järvelä, S. (2024). Learners’ Linguistic Alignment and Physiological Synchrony: Identifying Trigger Events that Invite Socially Shared Regulation of Learning. Journal of Learning Analytics, 1-18. https://doi.org/10.18608/jla.2024.8287
https://creativecommons.org/licenses/by-nc-nd/4.0/
© 2024 Journal of Learning Analytics. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
https://creativecommons.org/licenses/by-nc-nd/4.0/
© 2024 Journal of Learning Analytics. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
https://creativecommons.org/licenses/by-nc-nd/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202408085263
https://urn.fi/URN:NBN:fi:oulu-202408085263
Tiivistelmä
Abstract
The theory of socially shared regulation of learning (SSRL) suggests that successful collaborative groups can identify and respond to trigger events stemming from cognitive or emotional obstacles in learning. Thus, to develop real-time support for SSRL, novel metrics are needed to identify different types of trigger events that invite SSRL. Our aim was to apply two metrics derived from different data streams to study how trigger events for SSRL shaped group linguistic alignment (based on audio data) and physiological synchrony (based on electrodermal activity data). The data came from six groups of students (N = 18) as they worked face-to-face on a collaborative learning task with one cognitive and two emotional trigger events. We found that the cognitive trigger event increased linguistic alignment in task-description words and led to physiological out-of-synchrony. The emotional trigger events decreased out-of-synchrony and increased high-arousal synchrony at the physiological level but did not affect linguistic alignment. Therefore, different metrics for studying markers and responses to different types of trigger events are needed, suggesting the necessity for multimodal learning analytics to support collaborative learning.
The theory of socially shared regulation of learning (SSRL) suggests that successful collaborative groups can identify and respond to trigger events stemming from cognitive or emotional obstacles in learning. Thus, to develop real-time support for SSRL, novel metrics are needed to identify different types of trigger events that invite SSRL. Our aim was to apply two metrics derived from different data streams to study how trigger events for SSRL shaped group linguistic alignment (based on audio data) and physiological synchrony (based on electrodermal activity data). The data came from six groups of students (N = 18) as they worked face-to-face on a collaborative learning task with one cognitive and two emotional trigger events. We found that the cognitive trigger event increased linguistic alignment in task-description words and led to physiological out-of-synchrony. The emotional trigger events decreased out-of-synchrony and increased high-arousal synchrony at the physiological level but did not affect linguistic alignment. Therefore, different metrics for studying markers and responses to different types of trigger events are needed, suggesting the necessity for multimodal learning analytics to support collaborative learning.
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