Predicting Situation Awareness from Team Communications

Abstract

Given the importance of Situation Awareness (SA) in military operations, there is a critical need for a realtime, unobtrusive tool that objectively and reliably measures warfighters' SA in both training and operations. Just as the requirement for improved access to SA measures has become vital, it is now commonplace for military team communications to be mediated by technology, hence easily captured and available for analysis. We believe that team communications can be used to derive SA measures. To address this issue, we are developing the Automated Communications Analysis of Situation Awareness (ACASA) system. ACASA combines the explanatory capacity of the SA construct with the predictive and computational power of TeamPrints, to assess team and shared SA as well as other cognitive processes. TeamPrints is a system that combines computational linguistics and machine learning techniques coupled with Latent Semantic Analysis (LSA) to analyze team communication. In this paper, we present the findings from an exploratory evaluation of how well TeamPrints predicts SA from the team communications arising during a military training exercise.