Predicting aviation training performance with multimodal affective inferences
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it