The Artificial Social Intelligence for Successful Teams (ASIST) program explores human-machine teaming and machine social intelligence in a teaming context. DARPA envisions computer-based agents that observe their surroundings; build and maintain rich representations of the environment, team, and individuals; infer teammates’ goals (non-verbal behavior); predict teammates’ actions; and assist teams by planning interventions and executing them at appropriate times.

Many of the most popular intelligent training systems, including driving and flight simulators, generate user time series data. In this project, we try to present a comparison of representation options for two different student modeling problems: 1) early failure prediction and 2) classifying student activities. Data for this analysis was gathered from pilots executing simple tasks in a virtual reality flight simulator. We demonstrate that our proposed embedding which uses a combination of dynamic time warping (DTW) and multidimensional scaling (MDS) is valuable for both student modeling tasks.