All for one and one for all – How are students’ affective states and group-level emotion regulation interconnected in collaborative learning?

Törmänen, Tiina; Järvenoja, Hanna; Mänty, Kristiina

doi:10.1016/j.ijer.2021.101861

Cited by 19 publications

(14 citation statements)

References 45 publications

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“…The academic emotions framework (Pekrun et al, 2002) and the affective circumplex model (Russell & Barrett, 1999) provided the theoretical basis for differentiating expressions of positive and negative affect. The video coding scheme was based on the authors' previous work (Törmänen et al, 2021a; Törmänen et al, 2021b ). Before proceeding to final coding, seven videos (10%) were coded by two researchers, and unclear cases were discussed to refine the coding scheme.…”

Section: Methodsmentioning

confidence: 99%

“…In general, the available evidence suggests that students in learning situations experience quite low levels of arousal and that simultaneous high arousal among group members seems rare (Harley, Jarrell, et al, 2019;Malmberg et al, 2019;Pijeira-Díaz, Drachsler, Kirschner, et al, 2018). Instances of high arousal have been linked to both negative and positive affective states (Ahonen et al, 2018;Harley, Jarrell, et al, 2019;Malmberg et al, 2019;Törmänen et al, 2021b), to learning challenges and regulation of learning (Malmberg et al, 2019;Törmänen et al, 2021a), and to both low and high performance (Harley, Jarrell, et al, 2019;Mason et al, 2018;Pijeira-Díaz, Drachsler, Kirschner, et al, 2018;Pizzie & Kraemer, 2018). Also, changes in physiological states have been studied in relation to regulation of learning and emotion regulation.…”

Section: Valence and Activation As Dimensions Of Group Affective Statesmentioning

confidence: 99%

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Affective states and regulation of learning during socio‐emotional interactions in secondary school collaborative groups

Törmänen

Järvenoja

Saqr

et al. 2022

Brit J of Edu Psychol

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Background: Group affective states for learning are constantly formed through socio-emotional interactions. However, it remains unclear how the affective states vary during collaboration and how they occur with regulation of learning. Appropriate methods are needed to track both group affective states and these interaction processes. Aims: The present study identifies different socioemotional interaction episodes during groups' collaborative learning and examines how group affective states fluctuate with regulation of learning during these episodes. Sample: The participants were 54 secondary school students working in groups across four science learning sessions. Methods: Multichannel process data (video, electrodermal activity [EDA]) were collected in an authentic classroom. Groups' affective states were measured with emotional valence captured from video data, and activation captured as sympathetic arousal from EDA data. Regulation of learning was observed from the videotaped interactions. Results: The study disclosed four clusters of socio-emotional interaction episodes (positive, negative, occasional regulation, frequent regulation), which differed in terms of fluctuation of affective states and activated regulation of learning. These clustered episodes confirm how affective states are This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Methodsmentioning

confidence: 99%

Section: Valence and Activation As Dimensions Of Group Affective Statesmentioning

confidence: 99%

Affective states and regulation of learning during socio‐emotional interactions in secondary school collaborative groups

Törmänen

Järvenoja

Saqr

et al. 2022

Brit J of Edu Psychol

Self Cite

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“…First, regarding the highly-performed collaborative pattern (i.e., Cluster 1), we found that opinion expression after a series of operations and trials could form a foundation for deep-level knowledge construction and group regulation to achieve high-quality collaboration (Ouyang & Chang, 2019;Park et al, 2015). Moreover, compared to negative emotions (i.e., Cluster 3, 4), students' positive emotions might contribute to the high quality of collaboration, like Cluster 1 did (Törmänen et al, 2021). Furthermore, consensus reaching in argumentation is also the key to achieve a high-quality of collaboration (Straus, 2002).…”

Section: Discussionmentioning

confidence: 92%

Multimodal learning analytics of collaborative patterns during pair programming in higher education

Ouyang

2023

Int J Educ Technol High Educ

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Pair programming (PP), as a mode of collaborative problem solving (CPS) in computer programming education, asks two students work in a pair to co-construct knowledge and solve problems. Considering the complex multimodality of pair programming caused by students’ discourses, behaviors, and socio-emotions, it is of critical importance to examine their collaborative patterns from a holistic, multimodal, dynamic perspective. But there is a lack of research investigating the collaborative patterns generated by the multimodality. This research applied multimodal learning analytics (MMLA) to collect 19 undergraduate student pairs’ multimodal process and products data to examine different collaborative patterns based on the quantitative, structural, and transitional characteristics. The results revealed four collaborative patterns (i.e., a consensus-achieved pattern, an argumentation-driven pattern, an individual-oriented pattern, and a trial-and-error pattern), associated with different levels of process and summative performances. Theoretical, pedagogical, and analytical implications were provided to guide the future research and practice.

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“…Another study showed how negative de-activating emotions initiated emotional regulation behavior at a group level, and activating emotions led learners to assist and maintain current regulatory activities. In contrast, positive and neutral de-activating emotions rendered learners to observers and sometimes to objects of others' regulation (Törmänen et al, 2021).…”

Section: Emotion As Central Factor In Processes Of Learning Togethermentioning

confidence: 95%

Being in-sync: A multimodal framework on the emotional and cognitive synchronization of collaborative learners

Lawrence

Weinberger

2022

Front. Educ.

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Collaborative learners share an experience when focusing on a task together and coevally influence each other’s emotions and motivations. Continuous emotional synchronization relates to how learners co-regulate their cognitive resources, especially regarding their joint attention and transactive discourse. “Being in-sync” then refers to multiple emotional and cognitive group states and processes, raising the question: to what extent and when is being in-sync beneficial and when is it not? In this article, we propose a framework of multi-modal learning analytics addressing synchronization of collaborative learners across emotional and cognitive dimensions and different modalities. To exemplify this framework and approach the question of how emotions and cognitions intertwine in collaborative learning, we present contrasting cases of learners in a tabletop environment that have or have not been instructed to coordinate their gaze. Qualitative analysis of multimodal data incorporating eye-tracking and electrodermal sensors shows that gaze instruction facilitated being emotionally, cognitively, and behaviorally “in-sync” during the peer collaboration. Identifying and analyzing moments of shared emotional shifts shows how learners are establishing shared understanding regarding both the learning task as well as the relationship among them when they are emotionally “in-sync.”

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All for one and one for all – How are students’ affective states and group-level emotion regulation interconnected in collaborative learning?

Cited by 19 publications

References 45 publications

Affective states and regulation of learning during socio‐emotional interactions in secondary school collaborative groups

Affective states and regulation of learning during socio‐emotional interactions in secondary school collaborative groups

Multimodal learning analytics of collaborative patterns during pair programming in higher education

Being in-sync: A multimodal framework on the emotional and cognitive synchronization of collaborative learners

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