PATTRN: Predicting, Analyzing and Tracking Training Readiness and Needs

ABSTRACT: The capability to routinely collect, assess, format, predict, and track readiness, performance, and proficiency data from live aircraft, instrumented ranges, and distributed mission operations simulation environments is represents a unique and critical capability for the Air Force. Lumir Research Institute proposes to build the Predicting, Analyzing, and Tracking Training Readiness and Needs (PATTRN) tool, a software suite that will provide access to performance data from various environments regardless of the native format. PATTRN will collect data from various environments, translate the data from its native format into a common format, store the data, routinely assess and track readiness and predict future readiness or future training proficiency fall offs. PATTRN will enhance the capabilities of existing data processing tools by providing access to data from a wide variety of environments, and in a wide variety of formats, along with linking raw data to performance measurement and readiness models. The ultimate goal of PATTRN is to provide a data framework that is both site- and protocol-independent, thus enabling readiness and future proficiency assessment across environments. PATTRN will not only enable longitudinal studies of performance across a wide variety of environments, but will also contribute to the ongoing efforts to achieve greater interoperability. BENEFIT: The proposed PATTRN system will provide the following benefits: Data translation capability from esoteric data formats to a common data format. Interoperability with existing data processing tools. Standardized means of tagging data across discrete environments. Routine assessment of trainees"proficiency across multiple environments. Routine performance measurement evaluations across multiple environments. Predicting future training proficiency falloffs. The proposed PATTRN system has the following potential commercial applications: The system architecture will be applicable in other domains where multiple independent data formats exist (e.g., Navy). The capability to predict future proficiency gaps will be applicable to industries where the time required for a human to complete a routine task (such as UPS loading a truck, or an auto mechanic changing a transmission) are dependent upon the frequency at which the task is performed. A common data format is the gateway by which existing commercial systems may share data with DoD systems.