Predicting Undergraduate RPA Training (URT) Student Performance
DOI:
https://doi.org/10.37266/ISER.2022v10i2.pp102-108Keywords:
Undergraduate RPA Training, GPA, PCSM, STEM, Private Pilot's LicenseAbstract
This project is in conjunction with the 558th Flying Training Squadron, the only Undergraduate Remotely Piloted Aircraft (RPA) Training (URT) squadron. The 558th is currently seeing a trainee failure rate of approximately 6%. The project team aims to predict student performance at URT using econometric regression strategies analyzing initial student data for selected trainees and identifying at-risk students early in the training timeline. The 558th seeks to enhance the performance of their trainees by identifying student trends indicative of success and failure prior to URT, enabling them to provide attention and resources to students in need. The team’s goal is to provide the 558th with information that will help them reduce the failure rate at URT without requiring additional funding. It was determined that GPA, PCSM, STEM, and Private Pilot’s License (PPL) status to be significant indicators of student success.
References
Carretta, T. R. (2013). Predictive validity of pilot selection instruments for remotely piloted aircraft training outcome. Aviation, Space, and Environmental Medicine, 84(1), 47–53. https://doi.org/10.3357/asem.3441.2013
Carretta, T. R., Rose, M. R., & Barron, L. G. (2015). Predictive validity of UAS/RPA Sensor Operator Training Qualification measures. The International Journal of Aviation Psychology, 25(1), 3–13. https://doi.org/10.1080/10508414.2015.981487
Jenkins, P. R., Caballero, W. N., & Hill, R. R. (2022). Predicting success in United States Air Force pilot training using Machine Learning Techniques. Socio-Economic Planning Sciences, 79, 101121. https://doi.org/10.1016/j.seps.2021.101121
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