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Real-time cognitive-affective dynamics of failure feedback in a technology-based learning task
Helene Ackermann1,2, Anna L Lange3,4, Hanna Dumont5
1Department of Educational Sciences, Universität Potsdam, Potsdam, Germany. helene.ackermann@uni-potsdam.de.
Communications Psychology
|June 12, 2026
Summary
Automated cognitive and metacognitive failure feedback improves learning performance. Personalized feedback enhances overall outcomes, while effectiveness varies with learner characteristics and feedback design.
Area of Science:
- Educational Technology
- Human-Computer Interaction
- Cognitive Psychology
Background:
- Automated feedback is prevalent in technology-based learning.
- Understanding real-time learner feedback processing is crucial for optimizing learning environments.
- Humanoid robots are emerging as platforms for delivering automated feedback.
Purpose of the Study:
- To investigate the impact of automated cognitive and metacognitive failure feedback on learning performance.
- To examine how feedback characteristics and learner traits moderate feedback effectiveness.
- To compare different levels of feedback adaptivity (fixed, frequency-adaptive, personalized-adaptive).
Main Methods:
- Ninety adult participants completed a learning task under three feedback conditions.
- A generalized path model analyzed effects of failure feedback on immediate performance.
- Moderation effects of feedback and learner characteristics were assessed.
Main Results:
- Both cognitive and metacognitive failure feedback enhanced the probability of correct responses.
- Personalized-adaptive feedback improved overall performance compared to generic feedback.
- Learners with higher cognitive ability benefited less from cognitive feedback, while those experiencing boredom benefited more.
Conclusions:
- Automated failure feedback can positively influence learning performance.
- Feedback personalization and learner states significantly impact feedback effectiveness.
- A nuanced, temporally sensitive approach to automated feedback design is essential for maximizing learning gains.
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