Predicting aviation training performance with multimodal affective inferences

Affect influences learning and training through various cognitive, psychomotor and motivational processes. This research aims to examine the role of affect in aviation training. Participants’ (N = 19) affect and performance were examined in simulated aviation training while they performed ten tasks. Affective states were inferred from electrodermal activity, facial expression and NASA Taskload Index. Performance accuracy was graded with the rubrics provided by pilot instructors in CAE Inc. We found that arousal (inferred from electrodermal activity) positively predicted performance in the level 2 (easy) task (F(1, 17) = 7.408, p < 0.05, std β = 0.55). Mental workload (as measured from self-report) negatively predicted performance in the level 3 (medium difficulty) (F(1, 15) = 4.598, p < 0.05, std β = −0.54) and level 4 (difficult) tasks (F(1, 15) = 12.85, p < 0.01, std β = −0.73), controlling for affect valence and arousal. This research is a preliminary step to a reconsideration of affect in theoretical frameworks in aviation. It demonstrates a comprehensive assessment of affect in aviation training, which could provide guidelines for instructional interventions to improve the overall training experience and pilot performance.