David (Liwei) Lai

Title: An Exploration of a Testing Procedure for the Aviation Industry
Date: Wednesday, December 6th, 2023
Time: 1:00PM
Location: LIB 2020
Supervised by: Dr. Gary Parker and Dr. Tim Swartz

Abstract:In the aviation industry, pilot training and testing are 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 paper explores the application of a variant of the 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.

Keywords: Many-Facet Rasch Model; Bayesian Estimation; Competency-based Training and Assessment; Stan.