Detecting shared physiological arousal events in collaborative problem solving

Dindar, Muhterem; Järvelä, Sanna; Nguyen, Andy; Haataja, Eetu; İricioğlu, Ahsen Çini

doi:10.1016/j.cedpsych.2022.102050

Cited by 25 publications

(24 citation statements)

References 63 publications

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“…It appears that the emotional "in-sync" and "out-of-sync" moments -as identified by EDA analysis -correspond with cognitive "in-sync" and "out-of-sync" moments -as identified by discourse analysis -implying that learners' emotional fluctuations are prone to converge when they share the same focus and work collaboratively on the task. Similar findings were reported by Dindar et al (2022), where shared physiological arousal events were mostly found in collaborative task-related events. Moreover, this result is congruent with Louwerse et al's (2012) findings that synchronization in face-to-face communication occurs across different modalities.…”

Section: Cognitive Processes In Co-present Computer-supported Collabo...supporting

confidence: 89%

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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Section: Cognitive Processes In Co-present Computer-supported Collabo...supporting

confidence: 89%

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

Lawrence

Weinberger

2022

Front. Educ.

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“…Productive collaborative learning is a multifaceted interaction process that integrates cognitive, motivational and emotional components as the core of collaboration (Borge et al, 2018; Ludvigsen, 2016). Therefore, collaborative learning success requires joint efforts and social interactions to construct affective, cognitive and behavioural synchronicity, and shared understanding among group members (Cukurova et al, 2018; Dindar et al, 2022; Noroozi et al, 2013). Nevertheless, the sharedness is often challenging to get established to work towards the shared goals of the group (Hadwin et al, 2018).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

“…Collaborative learning refers to a social arrangement that involves groups of learners working together to solve problems or construct knowledge (Roschelle & Teasley, 1995). For collaborative learning to succeed, learners are required to develop a sense of sharedness (Dindar et al, 2022). They should collaboratively set shared goals (Hadwin et al, 2018), construct shared mental models about the task condition (Gijlers & de Jong, 2009), participate in shared monitoring and regulation of the group progress (Järvenoja et al, 2020), conduct the task and share the outcomes.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, this study aims to contribute new knowledge by collecting and analysing a big multimodal data from the authentic learning classroom to reveal the regulatory patterns of physiological and regulatory activities in collaborative learning thus indicating signals for regulatory trigger events. Regarding physiological activities in collaborative learning, recent research has suggested the relationship between the shared physiological arousal events (SPAEs) and collaborative problem‐solving activities (Dindar et al, 2022). SPAEs refer to a moment involving physiological arousals among the group members at the same time.…”

Section: Introductionmentioning

confidence: 99%

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Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics

Nguyen

Järvelä

Rosé

et al. 2022

Brit J Educational Tech

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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

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“…Features and tools available for to the learner in a palette, such as self-quizzing and typing a summary, identify when the learner makes metacognitive judgments about knowledge (Azevedo et al, 2018) and how that might change across different learning goals (Cloude et al, 2021b). An electro-dermal bracelet records signals documenting changes in skin conductance produced by sympathetic innervation of sweat glands, a signal for arousal that can be matched to the presence of external sensory stimuli (Lane and D'Mello, 2019;Messi and Adrario, 2021;Dindar et al, 2022). Eye movements operationalize what, when, where, and how long the learner attends to, scans, revisits, and reads (or rereads) content and consults displays, such as a meter showing progress toward goals (Taub and Azevedo, 2019;Cloude et al, 2020).…”

Section: Characteristics Of Multimodal Data Used To Reflect Srlmentioning

confidence: 99%

System design for using multimodal trace data in modeling self-regulated learning

et al. 2022

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Self-regulated learning (SRL) integrates monitoring and controlling of cognitive, affective, metacognitive, and motivational processes during learning in pursuit of goals. Researchers have begun using multimodal data (e.g., concurrent verbalizations, eye movements, on-line behavioral traces, facial expressions, screen recordings of learner-system interactions, and physiological sensors) to investigate triggers and temporal dynamics of SRL and how such data relate to learning and performance. Analyzing and interpreting multimodal data about learners' SRL processes as they work in real-time is conceptually and computationally challenging for researchers. In this paper, we discuss recommendations for building a multimodal learning analytics architecture for advancing research on how researchers or instructors can standardize, process, analyze, recognize and conceptualize (SPARC) multimodal data in the service of understanding learners' real-time SRL and productively intervening learning activities with significant implications for artificial intelligence capabilities. Our overall goals are to (a) advance the science of learning by creating links between multimodal trace data and theoretical models of SRL, and (b) aid researchers or instructors in developing effective instructional interventions to assist learners in developing more productive SRL processes. As initial steps toward these goals, this paper (1) discusses theoretical, conceptual, methodological, and analytical issues researchers or instructors face when using learners' multimodal data generated from emerging technologies; (2) provide an elaboration of theoretical and empirical psychological, cognitive science, and SRL aspects related to the sketch of the visionary system called SPARC that supports analyzing and improving a learner-instructor or learner-researcher setting using multimodal data; and (3) discuss implications for building valid artificial intelligence algorithms constructed from insights gained from researchers and SRL experts, instructors, and learners SRL via multimodal trace data.

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Detecting shared physiological arousal events in collaborative problem solving

Cited by 25 publications

References 63 publications

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

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

Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics

System design for using multimodal trace data in modeling self-regulated learning

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