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A multi-dimensional evaluation method for flight cadets based on weighted Phi-CDP.

Yunxiang Zhao1, Zejian Liang1, Haiwen Xu2

  • 1Faculty of Science, Civil Aviation Flight University of China, Chengdu, 641419, China.

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A new dynamic weighting scheme improves flight training assessments by accounting for checkpoint importance. This method enhances performance evaluation, providing better insights for training and risk management.

Keywords:
Category discrimination powerDynamic weightingFlight trainingMulti-dimensional evaluationPhi coefficient

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Area of Science:

  • Educational Measurement
  • Aviation Training Analytics
  • Machine Learning in Education

Background:

  • Traditional cumulative assessments in complex flight training environments often use equal weighting, failing to capture individual checkpoint contributions to trainee proficiency.
  • This can lead to inaccurate performance evaluations, suppressing high performers and inflating low performers, thus weakening instructional improvement and risk warning systems.

Purpose of the Study:

  • To develop and validate a multi-dimensional dynamic weighting scheme for flight training assessments.
  • To improve the accuracy, interpretability, and practical utility of trainee proficiency evaluations.

Main Methods:

  • Proposed a dynamic weighting scheme integrating Phi correlation, category discriminatory power (CDP), and dimension-level priority (β).
  • Phi correlation (via chi-square) quantifies checkpoint-grade association; CDP measures pass-rate disparity; β encodes expert-defined dimension priorities.
  • Weights were calculated via within-dimension normalization and inter-dimension aggregation.

Main Results:

  • On a development cohort, the dynamic weighting scheme improved Accuracy, Precision, Recall, and F1 scores from 0.90/0.98/0.91/0.94 (baseline) to near-perfect values.
  • The method demonstrated widened between-class margins and reduced within-class score dispersion.
  • An external test showed substantial outperformance over the baseline without parameter re-estimation (e.g., Accuracy 0.9162, F1 0.9563).

Conclusions:

  • Dynamic weighting significantly enhances the separability and interpretability of trainee performance data in flight training.
  • The proposed method offers practical decision support for remediation, risk alerts, and promotion reviews.
  • The lightweight and deployable nature of the scheme provides robust data support for full-cycle training and personalized interventions.