Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning

Dindar, Muhterem; Malmberg, Jonna; Järvelä, Sanna; Haataja, Eetu; Kirschner, Paul A.

doi:10.1007/s10639-019-10059-5

Cited by 33 publications

(23 citation statements)

References 57 publications

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“…First, not all studies systematically manipulated CL according to CLT (Morton et al, 2019). Second, the majority of studies did not combine physiological data with self-reported CL (Dindar et al, 2019;Larmuseau et al, 1552British Journal of Educational Technology Vol 51 No 5 2020. Moreover, it might be interesting to link physiological data with task performance across the different sets of exercises, as previous studies indicated that physiological adjustments ensure that task performance remains the same across different levels of complexity (Brouwer et al, 2014;Iani, Gopher, & Lavie, 2004).…”

Section: Shortcomings and Research Questionsmentioning

confidence: 99%

“…First, most studies did not systematically manipulate CL based on insights from CLT (Morton et al, 2019). Second, the majority of studies did not combine physiological data with self-reports and task performance (Dindar, Malmberg, Järvelä, Haataja, & Kirschner, 2019;Larmuseau et al, 2019). Third, there is no unambiguous answer to which physiological features are best in assessing CL (Larmuseau et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Multimodal learning analytics to investigate cognitive load during online problem solving

Larmuseau¹,

Cornelis²,

Lancieri³

et al. 2020

Brit J Educational Tech

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To have insight into cognitive load (CL) during online complex problem solving, this study aimed at measuring CL through physiological data. This study experimentally manipulated intrinsic and extraneous load of exercises in the domain of statistics, resulting in four conditions: high complex with hints, low complex with hints, high complex without hints and low complex without hints. The study had a within‐subject‐design in which 67 students solved the exercises in a randomized order. Self‐reported CL was combined with physiological data, namely, galvanic skin response (GSR), skin temperature (ST), heart rate (HR) and heart rate variability (HRV). Multiple imputation was used for handling missing data from resp. 16 and 19 students for GSR/ST and HR/HRV. First, differences between conditions in view of physiological data were examined. Second, we investigated how much variance of self‐reported CL and task performance was explained by physiological data. Finally, we investigated which features can be used to assess (objective) CL. Results revealed no significant differences between the manipulated conditions in terms of physiological data. Nonetheless, HR and ST were significantly related to self‐reported CL, whereas ST to task performance. Additionally, this study revealed the potential of ST and HR to assess high CL.

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Section: Shortcomings and Research Questionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multimodal learning analytics to investigate cognitive load during online problem solving

Larmuseau¹,

Cornelis²,

Lancieri³

et al. 2020

Brit J Educational Tech

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“…EDA has recently been used to observe emotions during educational processes like self-regulated and multimedia learning (Dindar et al, 2019;Mudrick, Taub, Azevedo, Price, & Lester, 2017) and reading (Meer, Breznitz, & Katzir, 2016). Dew et al (1984) used various measures of EDA and different scales to assess MAtrait and MAstate, but found no relation between EDA and MAstate, and only a small relation between EDA and MAstate for one of their measures of EDA.…”

Section: Assessing Mathematics State Anxietymentioning

confidence: 99%

A comparison of self-reports and electrodermal activity as indicators of mathematics state anxiety.

Strohmaier

Schiepe-Tiska

Reiss

2020

FLR

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In the present study with 86 undergraduate students, we related trait Mathematics Anxiety (MA) with two indicators of state anxiety: self-reported state anxiety and electrodermal activity (EDA). Extending existing research, we included appraisals of control and perceived value in hierarchical multiple regression analyses in accordance with the control-value theory of achievement emotions (Pekrun, 2006). Results showed that trait MA predicted self-reported state anxiety, while no additional variance was explained by including control and value. In contrast, we found no significant relation between trait MA and physiological state anxiety, but a significant, negative three-way interaction effect with control and value. Regression coefficients indicated that trait MA predicted physiological state anxiety, but only in the presence of negative perceived control and positive perceived value. Thus, our results support the control-value theory for physiological state anxiety, but not for self-reports. They emphasize the need to distinguish between trait and state MA, the advantages of adopting the control-value theory, and the benefits of using EDA recording as a supplemental assessment method for state anxiety.

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“…For example, an individual can be highly interested in working on a problem‐solving task with their teammates, but their task interest may decrease towards the end of the task if the task is too difficult or if the individual cannot get along with their teammates. Given the dynamic nature of metacognitive experiences, a common approach when studying them has been to assess them with single‐item self‐reports at multiple time points during a learning task (Dindar, Malmberg, Järvelä, Haataja, & Kirschner, 2020).…”

Section: Metacognitive Experiencesmentioning

confidence: 99%

“…Furthermore, negative group emotions were found to contribute to higher PS. Dindar et al ., (2020) investigated the PS of students in a high school physics course over a term. The course was composed of collaborative learning and problem‐solving sessions.…”

Section: Ps As An Indicator Of Cps Processesmentioning

confidence: 99%

What does physiological synchrony reveal about metacognitive experiences and group performance?

Dindar¹,

Järvelä²,

Haataja³

2020

Brit J Educational Tech

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There is a growing body of research on physiological synchrony (PS) in Collaborative Problem Solving (CPS). However, the current literature presents inconclusive findings about the way in which PS is reflected in cognitive and affective group processes and performance. In light of this, this study investigates the relationship between PS and metacognitive experiences (ie, judgement of confidence, task interest, task difficulty, mental effort and emotional valence) that are manifested during CPS. In addition, the study explores the association between PS and group performance. The participants were 77 university students who worked together on a computer‐based CPS simulation in groups of three. Participants’ electrodermal activity (EDA) was recorded as they worked on the simulation and metacognitive experiences were measured with situated self‐reports. A Multidimensional Recurrence Quantification Analysis was used to calculate the PS among the collaborators. The results show a positive relationship between continuous PS episodes and groups’ collective mental effort. No relationship was found between PS and judgement of confidence, task interest, task difficulty or emotional valence. The relationship between PS and group performance was also non‐significant. The current work addresses several challenges in utilising multimodal data analytics in CPS research and discusses future research directions.

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Matching self-reports with electrodermal activity data: Investigating temporal changes in self-regulated learning

Cited by 33 publications

References 57 publications

Multimodal learning analytics to investigate cognitive load during online problem solving

Multimodal learning analytics to investigate cognitive load during online problem solving

A comparison of self-reports and electrodermal activity as indicators of mathematics state anxiety.

What does physiological synchrony reveal about metacognitive experiences and group performance?

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