Tag Archives: Training

Half-Life Learning Curves in the Defense Acquisition Life Cycle


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Author: Adedeji B. Badiru

Learning curves are useful for assessing performance improvement due to the positive impact of learning. In recent years, the deleterious effects of forgetting have also been recognized. Workers experience forgetting or decline in performance over time. Consequently, contemporary learning curves have attempted to incorporate forgetting components into learning curves. An area of increasing interest is the study of how fast and how far the forgetting impact can influence overall performance. This article introduces the concept of half-life analysis of learning curves using the concept of growth and decay, with particular emphasis on applications in the defense acquisition process. The computational analysis of the proposed technique lends itself to applications for designing training and retraining programs for the Defense Acquisition Workforce.

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LATIST: A Performance Support Tool for Integrating Technologies into Defense Acquisition University Learning Assets


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Authors: Nada Dabbagh, Kevin Clark, Susan Dass, Salim Al Waaili, Sally Byrd, Susan Conrad,
Ryan Curran, Shantell Hampton, George Koduah, Debra Moore, and Capt James Turner, USMC

The Defense Acquisition Workforce is getting younger, and its educational expectations include using advanced and innovative learning technologies. The Defense Acquisition University (DAU) has fully embraced this generational trend and has partnered with several institutions to conduct research on Advanced Learning Technologies, or ALT. One such partnership is with George Mason University’s Instructional Technology Immersion Program. The partnership’s goal was to examine DAU’s current learning assets and identify processes and methods for utilizing innovative learning technology designs. This article summarizes this effort and describes the resulting online performance support tool called LATIST (Learning Asset Technology Integration Support Tool) developed to facilitate the understanding, selection, and integration of ALT by DAU faculty and staff.

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