Deep Recurrent Neural Network (RNN) has gained popularity in many sequence classification tasks. Beyond predicting a correct class for each data instance, data scientists also want to understand what differentiating factors in the data have contributed to the classification during the learning process. We present a visual analytics approach to facilitate this task by revealing the RNN attention for all data instances, their temporal positions in the sequences, and the attribution of variables at each value level. We demonstrate with real-world datasets that our approach can help data scientists to understand such dynamics in deep RNNs from the training results, hence guiding their modeling process.

Collective intelligence (CI) is a property of groups that emerges from the coordination and collaboration of members and predicts group performance on a wide range of tasks. Previous studies of CI have been conducted with lab-based groups in the USA. We introduce a new standardized online battery to measure CI and demonstrate consistent emergence of a CI factor across three different studies despite broad differences in (a) communication media (face-to-face vs online), (b) group contexts (short-term ad hoc groups vs long-term groups) and (c) cultural settings (US, Germany, and Japan). In two of the studies, we also show that CI is correlated with a group's performance on more complex tasks. Consequently, the CI metric provides a generalizable performance measure for groups that is robust to broad changes in media, context, and culture, making it useful for testing the effects of general-purpose collaboration technologies intended to improve group performance.