As demanding or unsuitable learning material unnecessarily strains learners’ cognitive resources, they can benefit from aligned instructional support, e.g., due to increasing expertise or states of frustration. When examining the current educational literature on tailored instructional support, we can notice that structured and formalized scaffolding procedures are still lacking.
Affect-adaptive training systems for safety-critical domains (2021 – present)
Affect-adaptive systems are capable of detecting and adequately responding to learners’ emotional states by adapting the learning experience, e.g., by providing additional support to mitigate frustration, or accelerate lesson plans to avoid boredom. Accounting for affective responses during training for safety-critical situations may help develop coping strategies and improve resilience. Research emerging from a dissertation in the SimTech Graduate Academy focused on mechanisms of effective alignment and paved the way for advances in affective computing.
Related publications
- Schmitz-Hübsch, A., Gruber, M. E., Diaz, Y., Wirzberger, M., & Hancock, P. A. (2024). Towards enhanced performance: An integrated framework of emotional valence, arousal, and task demand. Ergonomics. https://doi.org/10.1080/00140139.2024.2370440
- Schmitz-Hübsch, A., Becker, R., & Wirzberger, M. (2023). Emotion-performance relationship in safety-critical human-machine systems. Computers in Human Behavior Reports, 100364. https://doi.org/10.1016/j.chbr.2023.100364
- Schmitz-Hübsch, A., Becker, R., & Wirzberger, M. (2023). Personality traits in the emotion-performance-relationship in intelligent tutoring systems. In R. A. Sottilare & J. Schwarz (Eds.) Adaptive Instructional Systems. HCII 2023. Lecture Notes in Computer Science (pp. 60–75). Springer. https://doi.org/10.1007/978-3-031-34735-1_5
- Schmitz-Hübsch, A., Stasch, S.-M., Becker, R., Fuchs, S., & Wirzberger, M. (2022). Affective response categories – Towards personalized reactions in affect-adaptive tutoring systems. Frontiers in Artificial Intelligence, 5, 873056. https://doi.org/10.3389/frai.2022.873056 [PDF]