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English Flipped Classroom Teaching Mode Based on Emotion Recognition Technology.

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  • 1School of Foreign Languages, Xihua University, Chengdu, China.

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Summary

This study revises the English flipped classroom model using emotion recognition technology. Integrating emotion recognition improves learning engagement and teaching effectiveness by addressing current limitations.

Keywords:
English flipped classroomSVM algorithmfeature extractionimage emotion recognitionspeech emotion recognition

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Area of Science:

  • Education Technology
  • Artificial Intelligence
  • Affective Computing

Background:

  • The flipped classroom model is a growing trend in education but faces challenges like low engagement and poor interaction.
  • Current English flipped classroom implementations require practical testing and revision to enhance effectiveness.
  • Emotion recognition technology offers potential solutions to improve student engagement and interaction in educational settings.

Purpose of the Study:

  • To analyze speech, image, and audition emotion recognition technologies.
  • To revise the English flipped classroom teaching mode using emotion recognition.
  • To evaluate the effectiveness of different emotion recognition methods in an educational context.

Main Methods:

  • Analysis of speech, image, and audition emotion recognition techniques.
  • Application of the Support Vector Machine (SVM) algorithm for emotion recognition.
  • Comparison of one-to-one method versus dimension discretization for emotion recognition accuracy.

Main Results:

  • Dimension discretization methods demonstrated improved emotion recognition results compared to the one-to-one approach.
  • The recognition rate using different dimension classification methods was 2.6% higher than the one-to-one method.
  • Emotion recognition technology, particularly with dimension classification, shows potential for enhancing educational applications.

Conclusions:

  • The revised English flipped classroom model incorporating emotion recognition technology shows promise.
  • Emotion recognition, especially through dimension classification, can significantly improve the accuracy and effectiveness of educational tools.
  • Further practical testing and refinement are recommended for integrating emotion recognition into flipped classroom environments.