NETC Acquisition Planning Framework for Managing Training Delivery Requirements
MetadataShow full item record
In 2015, we conducted an analysis of the Science of Learning (SoL) literature to determine how the Navy might augment or modify its current practices with regard to training system development and employment to better account for how Sailors learn, how they develop expertise, and how they retain that expertise over time. The goal of that work was to develop a methodology that would inform the training system design process in a way that could predict training outcomes to a reasonable degree within the constraints that it could not require a major overhaul of the existing procedures that NETC currently uses. We reported the results of this study in (Darken & McDowell, 2017A). All training starts with recognition and identification of a human performance shortfall. Personnel need to be able to execute a specific set of tasks at a specified level of performance. The process ends with a training solution being developed and/or employed. However, what happens in between is the concern of this report. Under perfect conditions (e.g. adequate resources, time, and support) a training requirement is analyzed by identifying exactly what tasks need to be trained and exactly what level of performance is required. To be completed properly, this requires (a) a detailed task analysis to a usable level of detail, and (b) measures of performance (MOP) that are known to measure the skill level of the performer on the real world task. If we have a detailed task analysis and training objectives (the objective, trained performance level of the trainee), then these should inform the design of the training intervention. Here is where media selection occurs, sequencing of materials, and pacing of instruction that all make up the final course of instruction (COI). However, often the process is short-circuited by jumping directly from the requirement to the solution without analysis, or the analysis is abbreviated with a high-level task analysis that does not inform the training designer about the demands of the tasks to be trained.
RightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.
NPS Report NumberN16-N213-A
Showing items related by title, author, creator and subject.
CONVOLUTIONAL NEURAL NETWORKS FOR DETECTION AND CLASSIFICATION OF MARITIME VESSELS IN ELECTRO-OPTICAL SATELLITE IMAGERY Rice, Katherine (Monterey, CA; Naval Postgraduate School, 2018-12);The ability to locate and identify vessels of interest in satellite imagery plays a vital role in maintaining maritime security. Recent studies have demonstrated that convolutional neural networks can be used to automatically ...
Pascucci, Francis M. (Monterey, California: Naval Postgraduate School, 2014-06);This work focused on the design of a graphical user interface to improve instructional design models and decision support for Marine aviation training. Trainee performance data was collected, analyzed, and compared the ...
Peterson, Teal A. (Monterey, CA; Naval Postgraduate School, 2020-03);Convolutional neural networks (CNNs) are a powerful tool for identification of patterns and objects within imagery or video. Training CNNs that can generalize well to their intended target dataset can require large amounts ...