System for Tracking, Assessing, and Standardizing Human-performance (STASH) Phase II

With the migration towards integrated live, virtual, and constructive (LVC) training, there is an increasing need to understand human performance both within and across LVC environments.  The problem with current data formats is that they are often incompatible, with no common specification across systems. As a result, it is nearly impossible to effectively and efficiently identify, generate, extract, and track LVC performance data, thus hindering efforts to assess trainees performance during LVC training events and exercises. To address these issues, we will develop the System for Tracking, Assessing, and Standardizing Human-performance (STASH). STASH will enable the development, analysis, and usability of integrated performance measures from LVC environments in support of performance assessment and review by achieving: (1) a universal tagging specification for extracting LVC data into performance measures; (2) an integrated warehousing system for performance measures; (3) a common method for identifying the data type (system, observer, communication) and source (LVC) needed to produce useful and integrated performance assessment; and (4) an integrated LVC Dashboard for defining and identifying training objectives, and the associated scenario and performance measurement elements required to fulfill those training objectives, and then presenting the resultant integrated performance assessments to instructor/observers. BENEFIT: The System for Tracking, Assessing, and Standardizing Human-performance (STASH) will operate on disparate information sources from live, virtual, and constructive environments and combine, interpret, and transform the underlying raw data streams, or performance measures if those systems are available, to present a consolidated and coherent view of human performance. Our team will bring a unique and proven technology for representing theory-based frameworks for human performance in training, called Human Performance Markup Language (HPML). In addition, the team will use its experience in developing competency-based measures, leverage its existing measurement technologies as well as our experience with other performance measurement systems, and leverage our current work developing displays for viewing integrated performance measures. Finally, we will use our participation with organizations such as the Simulation Interoperability Standards Organization (SISO) and the Joint Advanced Distributed Learning Co-Lab (JADL) to encourage and introduce standards in performance measurement and experiential training.