Joint and coalition military training exercises are typically large, expensive events. They take months or years to plan, and sometimes require weeks to execute. After an exercise concludes, however, it can be difficult to quantify how much the individual participants learned or to identify what knowledge or skill gaps they might still possess.
This approach supplements military exercises with lightweight digital learning opportunities before, during, and after their execution. Additionally, performance data from across these various touchpoints are collected and learning analytics are used to derive learning outcomes. Gradually capabilities and guidance are matured for blending distributed learning into exercises across a series of joint and coalition training events.
About the Project
The MADLx project enhances joint and coalition training exercises by integrating distributed learning with computer-based and live training exercises. The benefits of this integration include improved learning outcomes, increased learning efficiency through improved convenience of instructional materials, and expanded readiness reporting through advanced learning analytics and their visualizations. In addition to these technical outcomes, this effort seeks to strengthen partnership relationships by executing case studies in collaboration with coalition military organizations.
Research prototypes have already been tested in several multinational exercises. Additional coalitions as well as domestic exercises are planned for the future. MADLx project exercise targets include the following:
- 2018: Viking 18, a ten-day event, was held across multiple networked sites, represented the first large-scale trial of the MADLx project.
- 2019: The Combined Joint Staff Exercise was organized by the Swedish Armed Forces in collaboration with the ADL Initiative. Exercise participants, operators, and the exercise evaluation team all reported positive readouts on the digital learning components. After the event, the ADL Initiative component has now become a formal component of the core exercise planning team.
- 2020 and 2021: The MADLx project has also identified exercises to address in conjunction with the U.S. Joint Staff’s Joint Knowledge Online program. These include Global Medic (Medical Readiness Training Command), and Bold Quest 20.2 and 21.1 (JSJ6).
MADLx in Practice: The VIKING 18 Exercise
One of the multinational exercises supported by the MADLx project is Viking 18, a ten-day event that was held in April 2018 across networked sites located in Brazil, Bulgaria, Finland, Ireland, Serbia, and Sweden. The Viking exercise series was first chartered in 1999 as a Swedish and U.S. initiative at NATO’s 50th Anniversary Summit. Since then, the Swedish Armed Forces and Folke Bernadotte Academy have hosted Viking eight times, and it has become the largest recurring civil-military relations exercise worldwide, with 61 countries and 80 organizations participating in 2018. The Viking 18 exercise involved approximately 2,500 people, including 1,300 trainees and additional operators, monitors and support staff. This exercise trained civilians, military, police, and nongovernmental organizations together to be better prepared for deployment to a crisis response mission.
Viking 18 was the first large-scale test of xAPI in a multinational exercise. Viking organizers sought to approach the 2018 exercise as a total learning experience, incorporating more than two dozen xAPI-enabled e-learning courses and a synchronous multinational computer assisted exercise simulation.
The exercise also enabled learning analytics for both individual and aggregated performance in the e-learning courses and for the exercise objectives. The e-learning developers created a web-based data visualization to analyze and display the results of aggregated xAPI-conformant data and non-xAPI data from the exercise’s simulation. This allowed exercise organizers and other stakeholders to trace the performance of trainees across periods of time and training objectives. Viking 18 demonstrated the viability of blending distributed learning into multinational exercises, integrating xAPI across diverse e-learning courseware, extracting xAPI from a non-compliant learning management system, executing learning analytics at a large scale, and visualizing disparate types of data in real time within a multinational training context.
ADL in Exercises
MADLx Supports Field Testing for Distributed Learning Technologies
December 22, 2020
ADL Integration into VIKING 18
April 20, 2018