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Case report: Quantitative recognition of virtual human technology acceptance based on efficient deep neural network

Xu Wang1, Charles Chen2

  • 1School of Government, Sun Yat-sen University, Guangzhou, China.

Frontiers in Neurorobotics
|November 17, 2022
PubMed
Summary
This summary is machine-generated.

Teachers

Keywords:
UTAUT modelacceptancedeep learningdigital virtual humanneural network algorithm

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Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Robotics Education

Background:

  • Robotics education is crucial for enhancing students' digital skills.
  • Teacher willingness to adopt robotics education impacts its effectiveness and sustainability.

Purpose of the Study:

  • To investigate factors influencing teachers' acceptance of robotics education.
  • To analyze the impact of performance expectations, effort expectations, community influence, and enabling conditions on acceptance.

Main Methods:

  • A modified UTAUT (Unified Theory of Acceptance and Use of Technology) model was used.
  • Deep learning algorithms were employed to analyze data from 269 teachers in Guangdong and Henan primary and secondary schools.
  • Correlation analysis was performed on four key dimensions: performance expectations, effort expectations, community influence, and enabling conditions.

Main Results:

  • Enabling conditions (correlation=0.422) and community influence (correlation=0.396) significantly impacted teacher acceptance of robotics education.
  • Performance expectations also positively influenced acceptance (correlation=0.290), while effort expectations showed a negative correlation (correlation=-0.144).
  • Deep learning models (mDAE, AmDAE) demonstrated reduced training times compared to existing autoencoder models.

Conclusions:

  • Enabling conditions and community influence are key drivers for teacher acceptance of robotics education.
  • Teacher acceptance is influenced by a combination of perceived benefits, social factors, and available resources.
  • Optimized deep learning models offer efficiency improvements for AI-driven educational research.