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Related Experiment Video

Updated: Jun 14, 2026

A Computerized Functional Skills Assessment and Training Program Targeting Technology Based Everyday Functional Skills
07:31

A Computerized Functional Skills Assessment and Training Program Targeting Technology Based Everyday Functional Skills

Published on: February 13, 2020

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
PubMed
Summary
This summary is machine-generated.

Automated cognitive and metacognitive failure feedback improves learning performance. Personalized feedback enhances overall outcomes, while effectiveness varies with learner characteristics and feedback design.

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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.