Effective Game-Based Training for Police Officer Decision-Making

Linking Missions, Skills, and Virtual Content

Published in: I/ITSEC 2020 Conference, Paper No. 20456 (2020)

Posted on RAND.org on March 12, 2021

by Timothy Marler, Susan G. Straus, Matthew L. Mizel, John S. Hollywood, Bob Harrison, Douglas Yeung, Kelly Klima, Matthew W. Lewis, Skip Rizzo, Arno Hartholt, et al.

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Often, the development of virtual training environments, specifically games for training, can focus on new technology and content development but insufficiently address underlying training goals. This paper reports the result of a two-year pilot study that developed a framework for implementing low-cost, game-based, virtual reality (VR) technology for training police officers to improve their decision-making under stress. Working closely with partners in the police training community, the study developed a method to ensure virtual training environments reflect intended training goals. This approach maps standard missions undertaken by police officers (e.g., responding to a domestic violence report), to detailed skills and knowledge required by the missions (e.g., communicate effectively at home threshold, assess safety of persons involved in dispute, identify sources of potential risk, actively de-escalate), to implementation within a virtual training environment. Once relevant skills and knowledge were identified, a small number of realistic, compelling training vignettes were developed to represent typical stressful scenarios that require rapid decision-making. These research-based vignettes were then developed into a prototype VR-based training prototype, or a "First-Person-Talker" game to train how to effectively de-escalate a domestic violence mission under stressful conditions. In turn, the prototype VR-scenario was piloted by members of a police department to elicit end-user feedback regarding how effective such a system would be to help officers become more prepared to handle rapidly escalating encounters in the field. Finally, structured methods are presented for deploying the consequent system in the context of current training curricula.

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