Optimizing Student Pilot Learning Through Skill Development Prediction and Tracking

Effective pilot training is critical for aviation safety, yet evaluating student performance objectively remains a persistent challenge. The Private Pilot License (PPL) training phase establishes the foundation for safe flight, where timely, data-driven feedback can significantly accelerate skill acquisition. Modern training aircraft equipped with advanced avionics (e.g., Garmin G1000) record flight telemetry, offering an opportunity to augment qualitative instructor evaluations with quantitative rigor. This paper introduces an integrated, data-driven system for evaluating and tracking student pilot performance throughout the PPL curriculum. The system automatically assesses maneuver execution quality using a scoring algorithm that penalizes both tolerance deviations and excessive control oscillations. By tracking performance longitudinally across 1,114 recorded maneuvers from 130 students, the system model skill development trends, identifies improvement rates, and flags learning stagnation. A simulator-based experiment (N=29) calibrating the scoring mechanism against expert pilot self-assessments (R = 0.371). Results reveal a positive linear learning trajectory (m = +0.40 points per attempt) during the primary skill acquisition phase. This approach provides a scalable, objective framework for enhancing training outcomes and ensuring future pilots are prepared through informed, adaptive instruction.