Mimic: visual analytics of online micro-interactions

We present Mimic, an input capture and visual analytics system that records online user behavior to facilitate the discovery of micro-interactions that may affect problem understanding and decision making. As aggregate statistics and visualizations can mask important behaviors, Mimic can help interaction designers to improve the usability of their designs by going beyond aggregates to examine many individual user sessions in detail. To test Mimic, we replicate a recent crowd-sourcing experiment to better understand why participants consistently perform poorly in answering a canonical conditional probability question called the Mammography Problem. To analyze the micro-interactions, the Mimic web application is used to play back user sessions collected through remote logging of client-side events. We use Mimic to demonstrate the value of using advanced visual interfaces to interactively study interaction data. In the Mammography Problem, issues like user confusion, low confidence, and divided-attention were found based on participants changing their answers, doing repeated scrolling, and overestimating a base rate. Mimic shows how helpful detailed observational data can be and how important the careful design of micro-interactions is in helping users to successfully understand a problem, find a solution, and achieve their goals.