Uncovering Patterns in Collaborative Interactions via Cluster Analysis of Museum Exhibit Logfiles.

A driving factor in designing interactive museum exhibits to support simultaneous users is that visitors learn from one another, via both observation and conversation. Such collaborative interactions among museum-goers are typically analyzed through manual coding of live- or video-recorded exhibit use. We sought to determine how log data from an interactive multi-user exhibit could indicate patterns in visitor interactions that could shed light on informal collaborative constructivist learning. We characterized patterns from log data generated by an interactive tangible tabletop exhibit using factors like pace of and the timing of “success events. Here we describe processes for parsing and visualizing log data and explore what these processes revealed about individual and group interactions with interactive museum exhibits. Using clustering techniques to categorize museum-goer behavior and heat maps to visualize patterns in the log data, we found that there were distinct trends in how users approached solving the exhibit: some players seemed more reflective while others seemed more achievement oriented. We also found that the most productive sessions occurred when all four areas of the table were occupied, suggesting that the activity design had a desired outcome to promote collaborative activity.