Abstract
Affective Tutoring Systems (ATS) detect and mitigate critical emotional learner states with the aim of providing individualized support. In tutoring systems for safety-critical work environments, students are trained to achieve and maintain high performance, therefore an ATS should be capable of identifying critical emotional states hindering performance. Interindividual differences in the emotion-performance-relationship can be considered by using the ARC categorization system. The present contribution aims at developing a questionnaire-based method of classifying new learners to the categories. To that end, we investigated differences in personality traits between the different categories. In an airspace surveillance task, we measured performance, emotional valence, emotional arousal, and personality traits in N = 50 subjects. Results showed that a positive valence-performance-relationship, compared to a negative valence-performance-relationship, is associated with higher Neuroticism, lower Conscientiousness, and lower Openness to experience. There were no significant differences in the traits Agreeableness and Extraversion. Based on these results, a future ATS for safety-critical work environments could classify new learners in the ARCs using self-report data and thus dispense with physiological sensors. Thereby, user state diagnosis and evaluation for high performance is possible, setting the ground for an ATS adapting to critical emotional learner states.
Users
Please
log in to take part in the discussion (add own reviews or comments).
