Description: 

The Periscope Operator Adaptive Trainer (POAT) is a computer-simulated periscope adaptive trainer, which focuses on the spatial task of calling angle on the bow.  To test whether game features can improve adaptive training, we created a new version of POAT that includes games features: leaderboard, score, and performance gauges.

​

My role:  Co-Principal Investigator

- Conducted experiments with university students and military end-users at the Submarine Officer Basic Course

- Developed research questions and experimental design to determine the efficacy of game features on learning from an adaptive computer-based simulation

- Co-authored a book chapter on the types of instructional feedback in serious games

- Bug tested extensively a modified build of POAT to train periscope operations (e.g., angle on the bow, range)

- Led statistical data analyses (Mixed Effects ANOVA and Regression) and interpreted results for publication
- Completed documents for the Institutional Review Board (IRB)

​

Awards:

2016 - Best Paper in Training at the Interservice/Industry Training, Simulation and Education Conference

 

Publications:

Johnson, C.I., Bailey, S.K.T., & Van Buskirk, W.L. (2017). Designing effective feedback messages in serious games and simulations: A research review. In P. Wouters & H. van Oostendorp (Eds.), Techniques to Improve the Effectiveness of Serious Games. Switzerland: Springer International Publishing.

​

Landsberg, C.R., Bailey, S.K.T., Van Buskirk, W.L., Gonzalez-Holland, E., & Johnson, C.I. (2016). Designing effective feedback in adaptive training systems. Proceedings of the Interservice/Industry Training, Simulation and Education Conference. *Best Paper in Training

​

​