When evaluating group performance in the field of group collaboration different forms of information sharing types are utilised as indicators for well performing or under performing groups. A transcript-file of a group conversation provide us with such communication patterns in form of the order of speaker turns and the amount of words assigned to each speaker at a particular instance. In this study we explore how one can utilise transcript meta data and the field of data visualisation to help a researcher to deduce insights for further investigation of the studied conversation faster. This would help qualitative researchers in the pre-analysis of bigger data sets. In Python we wrote a script that – from transcript meta-data – presents Temporal Static Visualisations (TSV) of the group members individual contribution as well as the mean-variance in participation over time. This supports the researcher in identifying sequences of and transitions between monologues, dialogues and group discussions in the observed conversation.

The main strength of TSV lies in the static inclusion of time development that helps the researcher to gain a quick insight into the flow of the observed conversation. However we still face the challenge of providing the design researcher with more contextual information. Thus, proposed future work should focus on how to automatically include additional context and content information such as artefact usage and physical interactions in the visualisations.