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Can human-machine feedback in a smart learning environment enhance learners' learning performance? A meta-analysis.

Mengyi Liao1, Kaige Zhu1, Guangshuai Wang2

  • 1School of Education, Pingdingshan University, Pingdingshan, Henan, China.

Frontiers in Psychology
|January 25, 2024
PubMed
Summary

Human-machine feedback in smart learning environments significantly improves learning processes and outcomes. Key factors influencing effectiveness include feedback direction, form, and technique, emphasizing two-way, multi-subject, and emotional feedback strategies.

Keywords:
feedback directionfeedback formfeedback technique typehuman-machine feedbackmeta-analysissmart learning environment

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Area of Science:

  • Educational Technology
  • Human-Computer Interaction
  • Learning Sciences

Background:

  • Human-machine feedback in smart learning environments impacts learner engagement and development.
  • Existing research on the stability and effectiveness of this feedback is often contradictory.
  • Understanding the conditions that optimize human-machine feedback is crucial for enhancing learning.

Purpose of the Study:

  • To systematically analyze the impact of human-machine feedback on learning performance.
  • To identify boundary conditions that moderate the effectiveness of human-machine feedback in smart learning.
  • To synthesize findings from empirical studies on human-machine interaction in education.

Main Methods:

  • A comprehensive meta-analysis was conducted on randomized controlled trials published between 2010 and 2022.
  • Searches were performed across major academic databases: Web of Science, EBSCO, PsycINFO, and Science Direct.
  • The random effects model was employed to assess the main effects and heterogeneity, with moderating effects analyzed to identify boundary conditions.

Main Results:

  • The meta-analysis included 35 articles with 2,222 participants, revealing significant positive effects of human-machine interaction feedback.
  • Feedback significantly enhanced learners' learning processes (d = 0.594) and learning outcomes (d = 0.407).
  • The positive impact was moderated by the direction, form, and type of feedback technique employed.

Conclusions:

  • Two-way and multi-subject feedback mechanisms are recommended for optimizing learning performance.
  • Prioritizing technologies offering emotional feedback and feedback loops is advised.
  • Focus on feedback processes, avoid over-reliance on machines, and foster learner autonomy.