Biosignals reflect pair-dynamics in collaborative work: EDA and ECG study of pair-programming in a classroom environment

Abstract

Collaboration is a complex phenomenon, where intersubjective dynamics can greatly affect the productive outcome. Evaluation of collaboration is thus of great interest, and can potentially help achieve better outcomes and performance. However, quantitative measurement of collaboration is difficult, because much of the interaction occurs in the intersubjective space between collaborators. Manual observation and/or self-reports are subjective, laborious, and have a poor temporal resolution. The problem is compounded in natural settings where task-activity and response-compliance cannot be controlled. Physiological signals provide an objective mean to quantify intersubjective rapport (as synchrony), but require novel methods to support broad deployment outside the lab. We studied 28 student dyads during a self-directed classroom pair-programming exercise. Sympathetic and parasympathetic nervous system activation was measured during task performance using electrodermal activity and electrocardiography. Results suggest that (a) we can isolate cognitive processes (mental workload) from confounding environmental effects, and (b) electrodermal signals show role-specific but correlated affective response profiles. We demonstrate the potential for social physiological compliance to quantify pair-work in natural settings, with no experimental manipulation of participants required. Our objective approach has a high temporal resolution, is scalable, non-intrusive, and robust.

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

“…In the context of collaborative learning, the dynamic and reciprocal adaptation of shared interaction emerges: that is, when individuals in a group are not only working on the same activity at the same time, but are also all "in tune" mentally (Baker 2002;Popov et al 2017). Therefore, synchronicity between individuals in collaborative learning can also be seen in gazes (Schneider and Pea 2013), joint visual attention (Schneider et al 2018), and physiology (Ahonen et al 2018;Gillies et al 2016).…”

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

“…Contradictory findings have been reported in the literature in terms of the relationship between PS and mental effort. For example, Guastello, Reiter, and Malon (2016) found a positive relationship between PS and perceived workload in a dual vigilance task, Ahonen, Cowley, Hellas, and Puolamäki (2018) observed a negative relationship between PS and perceived workload in a collaborative programming task, while Montague and Colleagues (2014) found no relationship between PS and mental effort in a vigilance task. These three studies investigated PS in dyadic collaboration.…”

“…Second, group synchrony in CPS can be measured using various types of multimodal data. For example, previous studies have utilised eye gaze (Cukurova et al ., 2018), facial expressions (Dindar, Järvelä, Ahola, Huang, & Zhao, 2019), hand movements (Wallot, Mitkidis, et al ., 2016), postural sway (Strang et al ., 2014), speech (Louwerse, Dale, Bard, & Jeuniaux, 2012) and heart rate (Ahonen et al ., 2018) to study synchrony in CPS. On the basis of the current findings, it can be inferred that the relationship between PS and group performance could be unearthed by deriving multiple PS metrics from different multimodal indicators of group behaviour and physiology.…”

Capturing motivation and emotion regulation during a learning process

“…Therefore, we next present an example from our current, on-going study to explore the possibilities of implementing physiological data in recognising and tracing emotion and motivation regulation in the course of collaborative learning. In the following example, we are measuring collaborative group members' electrodermal activity from the skin to depict temporal variations in their physiological arousal (see also Ahonen, Cowley, Hellas, & Puolamäki, 2018;Gillies et al, 2016). To contextualise the physiological data, we relate it with video data codings of valence in socio-emotional episodes (see example in Claim 2), which considered as potential indications of situations in which group members' joint co-and socially-shared emotion and motivation regulation can emerge.…”