# Initial Student Attention-Allocation and Flight-Performance Improvements Based on Eye-Movement Data
## 4 Conclusions  
In this study, we recruited 8 instructors and 30 novice pilots from the Guanghan Branch of Civil Aviation Flight University of China, an SR20 simulator was selected to conduct a flight course involving no-power stalls, and exceptional flight instructors were chosen based on flight-performance data. Standard attention-allocation charts and eye-tracking videos were created for the subjects, who each experienced a stall and performed a stall recovery; instructor eye-movement data were used as a basis for attention-allocation training courses. These training courses were then applied to an experimental group of trainees. Comparisons of the differences between three groups of subjects yielded three primary conclusions:  
1. During the no-power stall course, the instructors gazed longer at the tachometer than did the students, while they spent less time gazing at other instruments. This discrepancy can be attributed to the instructors' deeper understanding of the instruments and their more accurate information processing. 2. In the initial pilot sample data, a significant correlation was observed between the flight performance and the attention distribution. The correlation coefficients of horizontal status indicator, stall recovery time and heading offset are 0.609 and \(-0.7\) respectively, the correlation coefficients of airspeed indicator and lost speed are \(-0.615\), and the correlation coefficients of lost altitude and vertical speed indicator and altimeter are \(-0.667\) respectively and \(-0.521\). Specifically, longer gaze durations with respect to the horizontal status indicator during stall recovery were associated with longer stall durations and smaller course deviations. Similarly, longer fixation times on the airspeed indicator were associated with smaller speed losses, while longer gaze durations with respect to the vertical speedometer and the altimeter were associated with smaller altitude losses. Figure 9: Spearman correlation heat map(* \(p<0.05\)* \(p<0.01\)). 3. The attention-allocation training course implemented by the institute significantly improved the flight performance of the trainees. The difference between the trainees in the experimental group and the instructors in the attention distribution training is only in the gaze time of the airspeed indicator (\(p\) = 0.011), and there are more differences in flight performance in the control group than in the experimental group and the instructors (lost speed, \(p\) = 0.01). An analysis of the eye-movement-index data of the experimental group of students showed that the attention-distribution patterns of these students were more similar to those of the instructors after the training, and its instrument scanning was more strategic. Consequently, the flight performance of the experimental group of students surpassed that of the control group of students. These findings highlight the effectiveness of using attention-allocation training for enhancing flight performance and align with the objectives of this study. In the future, when analyzing eye movement data, machine learning methods can be added and combined with other physiological factors such as EEG to increase the reliability of the data; in terms of teaching, flight instructors can refer to the real-time attention of pilot students in subjects The allocation situation and objective parameters are used to correct the attention distribution of the pilot students in real time, so as to improve the quality of the flight training of the pilot students and promote aviation safety. Conceptualization: Y.Q. and J.Y. ; methodology: Z.Q. and X.C. ; software: Z.Q. and X.C. ; validation: Z.Q., Y.Q. and Z.S. ; formal analysis: Z.Q. and Y.Q. ; writing--original draft preparation: [PERSON]; writing--review and editing: [PERSON], Y.Q. and Z.S. ; supervision: [PERSON]; project administration: X.L. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China (No.