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Affective Response Categories-Toward Personalized Reactions in Affect-Adaptive Tutoring Systems.

Alina Schmitz-Hübsch1, Sophie-Marie Stasch2, Ron Becker1

  • 1Fraunhofer Institute for Communication, Information Processing and Ergonomics FKIE, Wachtberg, Germany.

Frontiers in Artificial Intelligence
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Summary

Affect-adaptive tutoring systems personalize learning by responding to student emotions. This study introduces Affective Response Categories (ARCs) to tailor adaptations for improved performance in safety-critical training.

Keywords:
affect-adaptive systemsaffective user statehierarchical cluster analysisintelligent tutoring systemssafety-critical systems

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Area of Science:

  • Educational Technology
  • Cognitive Science
  • Human-Computer Interaction

Background:

  • Affect-adaptive tutoring systems adjust learning experiences based on detected learner emotions.
  • Such systems can enhance training, especially in safety-critical domains, by fostering coping strategies and resilience.
  • Effective adaptation requires understanding how different emotions impact learning performance.

Purpose of the Study:

  • To introduce the Affective Response Categories (ARCs) model for classifying learners based on their emotion-performance relationships.
  • To investigate interindividual differences in how emotion (valence and arousal) affects performance in a simulated task.
  • To revise the ARC model based on empirical findings for improved adaptive tutoring.

Main Methods:

  • An experimental study with 50 participants performing a simulated airspace surveillance task.
  • Emotional valence was measured via facial expression analysis; emotional arousal was measured using pupil diameter.
  • Cluster analysis was used to group participants into ARCs based on emotion-performance correlations.

Main Results:

  • Three distinct clusters of learners were identified based on their emotion-performance relationships.
  • One cluster showed no correlation between emotion and performance.
  • The other two clusters demonstrated performance benefits from negative arousal, with varying valence-performance correlations (positive or negative).

Conclusions:

  • The Affective Response Categories (ARCs) model provides a framework for understanding individual differences in emotion-performance relationships.
  • The findings support the revision of the ARC model, highlighting the benefit of negative arousal for performance in specific learner groups.
  • This research has implications for designing more effective affect-adaptive tutoring systems, particularly in high-stakes training environments.