This US Army Basic Rifle Marksmanship case study began in 2013 with a white paper discussing training challenges. There was no uniform mechanism for collecting the available data from the various proprietary live ranges, and data was often abstracted to the unit level. They wanted to find better ways to provide meaningful feedback and training at the individual level and have the ability to securely aggregate data sets for analysis. If data from live fire exercises could be captured, correlated at the individual level, and made easily available for data mining, then the US Army could benefit from a wide array of analysis focused on live fire training. In addition, trainees could receive relevant and timely feedback: not only detailed training records, but also automated feedback or remediation based on their results delivered on-demand.

In this case study session, you’ll explore how xAPI connectors were created to parse the proprietary training data into xAPI and begin to enable the data collection and analysis the US Army required. You’ll discover that these connectors are not difficult or complex, and they prove the ability to take training performance data from multiple systems using xAPI and an LRS. This program allows for a standard practice of capturing xAPI data from live marksmanship systems, but you’ll also see how this approach can be used in other business training situations. This case study is an effort of the Army Research Lab (ARL) with senior research scientist Greg Goodwin.

In this session, you will learn:

• How xAPI connectors can parse proprietary training data into xAPI
• How this system analyzes the data stream for performance patterns and provides feedback to the trainee
• How this approach shows the ability to provide automated tutoring
• About the parallels that can be made for any business training case

Audience:

Developers, managers, senior leaders (directors, VP, CLO, executive, etc.).

Technology discussed in this session:

xAPI and open-source big data tools (Elastic Stack/Kibana).