An Integrated After Action Review (IAAR) approach: Conducting AARs for scenario-based training across multiple and distinct skill areas

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Abstract

Due to resource constraints, labor intensive scenario-based training solutions often include training on more than one skill area consisting of distinct multiple learning objectives. However, After Action Reviews (AARs) taking place after training have not adapted and have either become complex and drawn out to accommodate more skill areas or worse, critical objectives are simply left out because there is no time left to cover them. These AAR challenges should be addressed because each skill area and objective should be discussed for optimal learning and team performance improvements to occur. An Integrated AAR (IAAR) approach designed to cover multiple skill area objectives can enhance scenario based training opportunities without encumbering a team member’s ability to learn. During the Squad Overmatch (SOvM) training effectiveness evaluation different resources were developed to conduct an IAAR crossing multiple skill areas. Some of the resources developed worked well while others required revisions. The SOvM IAAR process and approach is described, lessons learned are discussed, and a new concept for an IAAR dashboard is presented.

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APA

Townsend, L., Johnston, J., Ross, W. A., Milham, L., Riddle, D., & Phillips, H. (2018). An Integrated After Action Review (IAAR) approach: Conducting AARs for scenario-based training across multiple and distinct skill areas. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10906 LNAI, pp. 230–240). Springer Verlag. https://doi.org/10.1007/978-3-319-91122-9_20

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