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.

Figure 1

(A) The students were arranged in dyads seated at a shared computer, in a series of sessions with differing numbers of students per session. (B) Electrodermal activity (EDA) and electrocardiograph (ECG) were recorded as shown. (C) The task involved a series of assignments with periodic role switching. (D) For first research questions (RQ1&2), social physiological compliance (SPC) was compared between dyads (dotted lines) and the general correlation of the classroom (long-dashed lines), estimated via a bootstrapping procedure.

Figure 2

(A) Left: The grand average of normalized skin conductance response (SCR) in driving and navigating roles with different outcomes in events. The two top rows of the left column illustrate the differences in passing and failing (line type difference) around running and testing events (time 0). The lower two rows of the left column illustrate effect of roles (colour difference) around the events (time 0). Dotted lines show naive p < 0.05 bootstrapped confidence intervals for each time point. (B) Right: The right column shows the grand averages of intra-participant differences in scaled SCR signals, corresponding to the respective plot in the left column. The participant-wise averages were sampled to find confidence intervals for the differences. Dashed lines show confidence bands derived using MWE corrected boundaries for p < 0.05 significance. Dotted lines show the naive 95% bootstrapped confidence intervals for each time point. The gray background marks time points where the signal level difference deviates from zero significantly.

Figure 3

The grand averages of SCR 10 second time-lagged difference in time windows of −10 to 10 seconds. The figure illustrates the change in signal levels in different conditions. Times of significant signal change have grey background. Dashed lines show confidence bands derived using MWE. The MWE’s contain 95% of the bootstrapped difference curves. Dotted lines show naive univariate 95% bootstrapped confidence intervals for each time point.

Figure 4

Using same method as for Fig. 3, SCR time lagged difference in curves for Passed-outcome × role conditions, using separate data from room 1 (top row) and room 4 (bottom row). The most interesting differences between rooms are in the conditions shown.