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

Abstract

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.

A New Era in Multimodal Learning Analytics: Twelve Core Commitments to Ground and Grow MMLA

“…Pervasive across many MMLA studies is the goal of surfacing aspects of learning that are hard to see. Existing work with eyetracking and electro-dermal activation is a hallmark of this commitment, in that they incorporate modalities that offer a level of specificity not easily attained through human observation (Abrahamson, Shayan, Bakker, & Van Der Schaaf, 2016;Dindar, Järvelä, & Haataja, 2020;Huang, Bryant, & Schneider, 2019;Jermann, Gergle, Bednarik, & Brennan, 2012;Sharma, Giannakos, & Dillenbourg, 2020). In the case of eye gaze, humans can broadly perceive where someone is looking, but typically not at the level or frequency provided by eye-tracking technology.…”

“…However, the practice of normalizing multimodal data may require greater awareness of the underlying science about how the multimodal technology or modelling algorithms work. Within the MMLA community, this normalization process has frequently been applied with audio (Bassiou et al, 2016), electro-dermal activation (Dindar et al, 2020;Worsley & Blikstein, 2018), and facial expression (Grafsgaard et al, 2014;Worsley, Scherer, Morency, & Blikstein, 2015) analysis, as well as gesture classification (Schneider & Blikstein, 2015;Worsley & Blikstein, 2013). Given the extensive research on bias in facial expression and face recognition analysis (Xu, White, Kalkan, & Gunes, 2020), based on race, gender, and ethnicity, for example, there is an unmistakable need to effectively normalize the data and account for individual and group differences.…”

Multimodal data capabilities for learning: What can multimodal data tell us about learning?

“…For example, Larmuseau, Cornelis, Lancieri, Desmet, and Depaepe ( 2020 ) measure cognitive load ( behavioral trajectories ) using GSR, Electrocardiography (ECG) and HRV in an informal online problemsolving task. Dindar, Oulun, Järvelä, Haataja, and Oulun ( 2020 ) monitor meta-cognitive experiences ( behavioral trajectories ) and explain group performance ( learning performance ) using data coming from learners' EDA and survey responses in an informal collaborative problem-solving task. Olsen, Sharma, Rummel, and Aleven ( 2020 ) use LSTM on data features coming from learners' gaze, log, dialogue and audio to predict their posttest score ( learning performance ) and leaning gain ( learning outcomes ) in a formal collaborative setting of an ITS.…”

The promise and challenges of multimodal learning analytics

“…In addition to the nuances on the intended autonomy of the learning support platform, MMLA research is not limited to affective aspects of computing and equally relevant to the cognitive and metacognitive aspect of learning. For instance, in this special issue, Dindar, Oulun, Järvelä, Haataja, and Oulun (2020) investigated the relationship between electrodermal activity (EDA) and a number of self‐reported metacognitive experiences and learning performance amongst small collaborative problem‐solving groups with the help of MMLA. The authors found that the only consistent result was the measure of physiological synchrony and the mental effort required by a group of learners during a collaborative problem‐solving task.…”