Predictive Models of User Performance for Marksmanship Training
Blink, Mary Jean (TutorGen, Inc.) | Carmichael, Ted (TutorGen Inc., University of North Carolina at Charlotte) | Murphy, Jennifer (Quantum Improvements Consulting) | Eagle, Michael (Carnegie Mellon University, TutorGen Inc.)
How the Army conducts rifle marksmanship training is undergo-ing a number of positive changes. Despite this, challenges to con-ducting and coordinating this critical training remain. One chal-lenge to assessing training effectiveness is a lack of persistent records of soldier performance; too often soldier data are purged shortly after training events for convenience and in order to en-sure privacy. This paper reports on our efforts to research the fea-sibility of collecting, analyzing, and storing data from multiple training systems, in order to accelerate and improve marksman-ship training. We do this through the use of cognitive, psychomo-tor, and affective constructs; and the use of predictive modeling techniques in order to forecast marksmanship qualification scores.These models successfully predicted scores on a 40-point scalewith a root mean square error (RMSE) of less than three, using models that are robust to changing input variables. Future im-provements and directions for this research are also discussed.
May-17-2018
- Genre:
- Research Report (0.73)
- Industry:
- Government > Military > Army (0.73)
- Technology:
- Information Technology
- Artificial Intelligence > Machine Learning (0.53)
- Data Science > Data Mining (0.40)
- Modeling & Simulation (1.00)
- Information Technology