Stealth Optimized, Adaptive Assessments for Multistage UAS Operator Selection (Stealth Adapt) Navy SBIR FY2013.1 Sol No.: Navy SBIR FY2013.1 Topic No.: N131-082 Topic Title: Stealth Optimized, Adaptive Assessments for Multistage UAS Operator Selection (Stealth Adapt) Proposal No.: N131-082-0601 Firm: BattlePulse Technologies 3492 Kayla Circle Oviedo, Florida 32765 Contact: Phillip Mangos Phone: (321) 262-6649 Abstract: Unmanned Aerial System (UAS) operation represents a stressful, cognitively challenging domain where operators are routinely subjected to both occupational and combat stressors and performance failures can have catastrophic effects. Effective performance in such conditions has many dimensions, including technical proficiency, probability of catastrophic failures, mission productivity, resistance to stress symptomology, teamwork, and long-term work engagement. Effective selection methods for UAS operators should accurately, efficiently, and holistically predict these key outcomes. Our solution to this challenge is to develop a customized suite of 1) assessments measuring cognitive skills, non-cognitive attributes and operational stress coping processes embedded within 2) a novel, adaptive, multistage content delivery and protection framework, and 3) optimized via stealth scoring optimization techniques. One key innovation will be a suite of scoring algorithms grounded in data mining advances designed to boost performance prediction. These will be embedded in performance-based assessments that simulate tasks placing considerable demands on executive-level cognitive skills (mental simulation, task prioritization, and real-time replanning). Phase I deliverables, (KSAO ontology, assessment content, storyboards, scoring and adaptive delivery algorithms, cut score simulations), will provide a preview of the full Phase II content suite, and lay the foundation for transition to UASISST and platform-specific systems for unmanned aviation. Benefits: 1. Accurate performance prediction enabled by efficient, holistic assessment of a range of personal attributes designed to collectively predict overall UAS operator effectiveness, with particular emphasis on adaptive management of operational stress. 2. Minimization of assessment time and content protection. 3. Performance-based assessments measuring latent cognitive traits while affording uninterrupted, immersive, and realistic gameplay of simulated UAS tasks 4. Cross-platform, assessment agnostic scoring optimization algorithms to boost the operational validity of assessments developed for UAS operator personnel selection and provide the basis for dual-use scoring. 5. Utility optimization via the use of criterion-referenced cut scores and evaluation of thousands of cut score combinations. Return