# Collaborative System Usability in Spaceflight Analog Environments through Remote Observations
## 5 Discussion  
Our method, rooted in conversation analysis, aimed to comprehensively characterize collaboration among spaceflight analog crew members during timeline self-scheduling. Leveraging voice logs for unobtrusive monitoring, we remotely studied crew members' \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  &
\begin{tabular}{c} **Duration in** \\ **Minutes** \\ \end{tabular} & **CRTimeline** & **CRTask** & **CRPaybook** & **OT** \\ \hline Team Preference & 9.08 (6.20) & 13.93 (10.11) & 3.21 (3.17) & 2.14 (2.92) & 3.57 (2.12) \\ \hline Self-Scheduling & 28.91 (19.25) & 16.53 (19.11) & 2.19 (1.98) & 3.13 (5.80) & 6.66 (7.86) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics for counts of each category and overall durations across TPM and SS. The two skipped TPMs were not included in our calculations. Statistics are formatted as mean (standard deviation). Figure 4: Average UEQ scores for Playbook from in-lab testing (n = 30) and HERA C6 (n = 16) mapped onto benchmark categories. Error bars represent the 95% confidence intervals. verbal social interactions as they participated in team preference meetings (TPMs) and self-scheduling sessions (SS). We observed a general downward trend in the duration of TPMs over time. Crews appeared to dedicate progressively less time to discussing preferences throughout their missions, with two crews forgoing some TPMs in the latter half of their mission. No crew used the full duration allocated for any TPMs, and all latter TPMs had durations of only a few minutes. After reviewing the transcripts, we were unable to find explicit content in discussions that explained this trend; however, we inferred that crews became more efficient at discussing their preferences or that these preferences remained generally static after the first couple of discussions. This insight will inform future spaceflight analog missions, and we plan to allocate less time for these meetings before the self-scheduling sessions. It may be adequate to allocate a single longer block for the first meeting and shorter meetings to update and re-discuss preferences every week afterward. We also found that crew collaboration predominantly stayed on task during TPMs, focusing on time preferences rather than delving into Playbook features, the nature of the task at hand, or unrelated topics. This suggests that task instructions for these TPMs were clear, there were no critical Playbook usability issues, and assigned planners utilized the time to understand everyone's preferences before creating the team's schedule. Surprisingly, our conversation analysis revealed an unexpected aspect of SS. The assigned crew scheduler engaged in impromptu conversations with the rest of the crew to integrate team preferences into the timeline. Without the use of conversational analysis, we never would have discovered that crews were collaborating during SS. This collaborative behavior was unexpected since the SS task was designed for a single scheduler to independently create a feasible timeline for the whole team. We designed the experiment expecting the scheduler to have discussed these preferences before (during TPM) and then self-schedule alone. As mentioned earlier, this tendency for sporadic collaboration varied greatly across planners and crews, but almost all SS contained some type of timeline-related collaboration. For the first time, we have quantitative data that show that self-scheduling is a collaborative task. Similarly to the TPMs, the discussions during SS often stayed focused on timeline preferences. If much of the discussion revolved around maneuvering within Playbook's user interface, we would have interpreted that as a signal of some design flaw that required intra-crew collaboration to solve. If much of the discussion centered on the nature of the task itself or irrelevant topics, we would have interpreted that as confusing or unengaging task demands.