Application developers frequently augment their code to produce event logs of specific operations performed by their users. Subsequent analysis of these event logs can help provide insight about the users' behavior relative to its intended use. The analysis process typically includes both event organization and pattern discovery activities. However, most existing visual analytics systems for interaction log analysis excel at supporting pattern discovery and overlook the importance of flexible event organization. This omission limits the practical application of these systems. Therefore, we developed a novel visual analytics system called IntiVisor that implements the entire end-to-end interaction analysis approach. An evaluation of the system with interaction data from four visualization applications showed the value and importance of supporting event organization in interaction log analysis.

Abstract Developmental psychology researchers examine the temporal relationships of social and communicative behaviors, such as how a child responds to a name call, to understand early typical and atypical development and to discover early signs of autism and developmental delay. These related behaviors occur together or within close temporal proximity, forming unique patterns and relationships of interest. However, the task of finding these early signs, which are in the form of atypical behavioral patterns, becomes more challenging when behaviors of multiple children at different ages need to be compared with each other in search of generalizable patterns. The ability to visually explore the temporal relationships of behaviors, including flexible redefinition of closeness, over multiple social interaction sessions with children of different ages, can make such knowledge extraction easier. We have designed a visualization tool called TipoVis that helps psychology researchers visually explore the temporal patterns of social and communicative behaviors. We present two case studies to show how TipoVis helped two researchers derive new understandings of their data.

We describe a system for detecting moments of eye contact between an adult and a child, based on a single pair of gaze-tracking glasses which are worn by the adult. Our method utilizes commercial gaze tracking technology to determine the adult's point of gaze, and combines this with computer vision analysis of video of the child's face to determine their gaze direction. Eye contact is then detected as the event of simultaneous, mutual looking at faces by the dyad. We report encouraging findings from an initial implementation and evaluation of this approach.