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Using deep learning-based artificial intelligence electronic images in improving middle school teachers' literacy.

Yixi Zhai1, Liqing Chu1, Yanlan Liu1

  • 1School of Foreign Studies, Tangshan Normal University, Tangshan City, China.

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|April 25, 2024
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Summary

Artificial intelligence (AI) enhances teacher training through electronic images. The Structure of the Observed Learning Outcome (SOLO) algorithm shows higher accuracy and lower complexity than SVM in assessing teacher cognitive literacy.

Keywords:
SOLO classificationArtificial intelligenceElectronic imagingImage recognitionTeacher’s intelligent literacy

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Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Cognitive Science

Background:

  • Electronic educational resources are crucial in modern education.
  • Secondary school teachers face increasing challenges in adapting to new technologies.
  • Artificial intelligence (AI) offers potential solutions for enhancing teacher cognitive literacy.

Purpose of the Study:

  • To analyze and explore the application of AI methods for enhancing secondary school teachers' cognitive literacy.
  • To investigate the role of AI-generated electronic images in teacher training and instruction.
  • To develop and evaluate an intelligent model for assessing teacher cognitive literacy and teaching efficacy.

Main Methods:

  • Analysis of AI-generated electronic images in middle school teacher training.
  • Development of an intelligent classification model using the Structure of the Observed Learning Outcome (SOLO) algorithm on an integrated intelligent device.
  • Comparison of the SOLO algorithm with Support Vector Machine (SVM) and decision tree algorithms for assessing teacher cognitive literacy.

Main Results:

  • The SOLO algorithm achieved 80% accuracy in recognizing teacher literacy after 600 iterations, outperforming the SVM algorithm's 77% accuracy.
  • The SOLO-based model demonstrated significantly reduced spatial complexity (22) compared to the SVM-based model (45).
  • Increasing iterations led to higher accuracy and lower spatial complexity for the SOLO algorithm in evaluating pedagogical literacy.

Conclusions:

  • AI methodologies are highly effective in advancing electronic imaging technology for educational purposes.
  • The SOLO algorithm provides a more accurate and efficient approach to assessing teachers' cognitive and pedagogical literacy compared to traditional methods like SVM.
  • AI-powered tools can significantly enhance teacher training and improve the overall efficacy of educational instruction.