On the Need of New Methods to Mine Electrodermal Activity in Emotion-Centered Studies

Henriques, Rui; Paiva, Ana; Antunes, Cláudia

doi:10.1007/978-3-642-36288-0_18

Cited by 24 publications

(14 citation statements)

References 26 publications

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“…In this regard, multimodal approaches in SRL research can help tackle the constraints of typical single-channel data and help draw more valid and reliable inferences about the learning processes (Harley et al 2015). While subjective measures (e.g., self-report data and interviews) explore the intent and appraisal of student learning, objective data (e.g., log data, eye tracking, heart rate, electro-dermal activity, videos, and facial expressions) can provide "on-the-fly" information about what students do when studying and can detect phases of challenge, interest, and attention (Henriques et al 2013). Although interest in applying multimodal data has increased in SRL, SSRL, and CSCL research, it is still in the early stages.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Capturing the dynamic and cyclical nature of regulation: Methodological Progress in understanding socially shared regulation in learning

Järvelä

Järvenoja

Malmberg

2019

Intern. J. Comput.-Support. Collab. Learn

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Self-regulation is critical for successful learning, and socially shared regulation contributes to productive collaborative learning. The problem is that the psychological processes at the foundation of regulation are invisible and, thus, very challenging to understand, support, and influence. The aim of this paper is to review the progress in socially shared regulation research data collection methods for trying to understand the complex process of regulation in the social learning context, for example, collaborative learning and computer-supported collaborative learning. We highlight the importance of tracing the sequential and temporal characteristics of regulation in learning by focusing on data for individual-and group-level shared regulatory activities that use technological research tools and by gathering in-situ data about students' challenges that provoke regulation of learning. We explain how we understand regulation in a social context, argue why methodological progress is needed, and review the progress made in researching regulation of learning.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Capturing the dynamic and cyclical nature of regulation: Methodological Progress in understanding socially shared regulation in learning

Järvelä

Järvenoja

Malmberg

2019

Intern. J. Comput.-Support. Collab. Learn

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“…Since physiological signals are sensitive to contextual changes, they hold the potential to advance empirical research on regulated learning (Azevedo 2015). That is, they can provide information related to cognitive demands and task difficulty and increased attention related to task engagement (Henriques et al 2013). For example, in the context of collaboration, individual learners' physiological reactions are dependent on and shaped by other learners in the same situation (Gillies et al 2016;Palumbo et al 2016).…”

Section: Collaborative Learning and Physiological Synchronymentioning

confidence: 99%

Are we together or not? The temporal interplay of monitoring, physiological arousal and physiological synchrony during a collaborative exam

Malmberg

Haataja

Seppänen

et al. 2019

Intern. J. Comput.-Support. Collab. Learn

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The coordination of cognitive and non-cognitive interactive processes contributes to successful collaboration in groups, but it is hard to evidence in computer-supported collaborative learning (CSCL). Monitoring is a metacognitive process that can be an indicator of a student's ability to recognize success or failure in collaboration. This study focuses on how monitoring occurs in CSCL during a collaborative exam situation by examining how individual student contributions to monitoring processes are related to physiological synchrony and physiological arousal in groups. The participants were organized in four groups of three members each, and they wore sensors that measured their physiological activity. The data consist of video recordings from collaborative exam sessions lasting 90 minutes and physiological data captured from each student with Empatica 4.0 sensors. The video data were analyzed using qualitative content analysis to identify monitoring events. Students' physiological arousal was determined through peak detection, and physiological concordance was used as an index for the students' physiological synchrony. The individual and group level analysis investigated arousal and physiological synchrony in concordance with monitoring during the collaborative exam. The results showed that, in each group, each student contributed to joint monitoring. In addition, the monitoring activities exhibited a significant correlation with the arousal, indicating that monitoring events are reflected in physiological arousal. Physiological synchrony occurred within two groups, which experienced difficulties during the collaborative exam, whereas the two groups who had no physiological synchrony did not experience difficulties. It is concluded that physiological synchrony may be a new indicator for recognizing meaningful events in CSCL

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“…This is unfortunate because objective measures offer various new venues for learning research. For example, it is known that physiological signals inform on specific cognitive or emotional challenges during learning (Henriques et al 2013). In this regard, objective data can be combined with subjective data to explain the sequence of specific micro-level processes that result in particular perceptions, feelings, and other learning-related outcomes.…”

Section: Characteristics Of Multimodal Data Studiesmentioning

confidence: 99%

“…The under-exploration of emotions in multimodal learning research is also reflected in the limited usage of certain data modalities. For example, facial recognition, electrodermal activity, heart rate variability, blood volume pulse, and body temperature were rarely used though they can be indicative of emotional states in the human mind and body (Henriques et al 2013). Utilising physiological measures in future learning research might open new paths to increase our knowledge, particularly on the emotional aspects of learning.…”

Section: Characteristics Of Multimodal Data Studiesmentioning

confidence: 99%

Multimodal data indicators for capturing cognitive, motivational, and emotional learning processes: A systematic literature review

et al. 2020

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This systematic review on data modalities synthesises the research findings in terms of how to optimally use and combine such modalities when investigating cognitive, motivational, and emotional learning processes. ERIC, WoS, and ScienceDirect databases were searched with specific keywords and inclusion criteria for research on data modalities, resulting in 207 relevant publications. We provide findings in terms of target journal, country, subject, participant characteristics, educational level, foci, type of data modality, research method, type of learning, learning setting, and modalities used to study the different foci. In total, 18 data modalities were classified. For the 207 multimodal publications, 721 occurrences of modalities were observed. The most popular modality was interview followed by survey and observation. The least common modalities were heart rate variability, facial expression recognition, and screen recording. From the 207 publications, 98 focused exclusively on the cognitive aspects of learning, followed by 27 publications that only focused on motivation, while only five publications exclusively focused on emotional aspects. Only 10 publications focused on a combination of cognitive, motivational, and emotional aspects of learning. Our results plea for the increased use of objective measures, highlight the need for triangulation of objective and subjective data, and demand for more research on combining various aspects of learning. Further, rather than researching cognitive, motivational, and emotional aspects of learning separately, we encourage scholars to tap into multiple learning processes with multimodal data to derive a more comprehensive view on the phenomenon of learning.

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On the Need of New Methods to Mine Electrodermal Activity in Emotion-Centered Studies

Cited by 24 publications

References 26 publications

Capturing the dynamic and cyclical nature of regulation: Methodological Progress in understanding socially shared regulation in learning

Capturing the dynamic and cyclical nature of regulation: Methodological Progress in understanding socially shared regulation in learning

Are we together or not? The temporal interplay of monitoring, physiological arousal and physiological synchrony during a collaborative exam

Multimodal data indicators for capturing cognitive, motivational, and emotional learning processes: A systematic literature review

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