ABOUT THE PROJECT
Below you’ll find information about MADLx project.
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.