In the aviation industry, pilot training is paramount, necessitating robust and precise assessment methodologies. Despite the shift towards Competency-based Training and Assessment (CBTA) recommended by the International Civil Aviation Organization (ICAO), there is a notable absence of comprehensive statistical models to substantiate the evaluation process. This project explores the application of an enhanced Many-Facet Rasch Model (MFRM) employing Bayesian estimation techniques and presents a novel approach for quantifying pilot competency scores, ensuring a more granular and accurate assessment of pilot capabilities. By analyzing simulated data, the research assesses the viability of this statistical approach in operational settings. Potential applications and limitations of this methodology within the aviation industry are discussed.
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