Evaluation and Stratification for Chinese International Education Quality with Deep Learning Model

Area of Science:

• Computational Linguistics
• Artificial Intelligence
• Educational Technology

Background:

• The increasing global demand for learning Chinese necessitates robust methods for evaluating international Chinese education quality.
• Current feature classification networks in deep learning often suffer from accuracy loss due to reliance on single-layer features.
• Effective evaluation is crucial for enhancing the quality and global dissemination of Chinese language education.

Purpose of the Study:

• To address the limitations of existing classification networks in evaluating international Chinese education.
• To design and implement a novel neural network for accurate quality assessment.
• To leverage deep learning for improving the standards of international Chinese language education.

Main Methods:

• Development of a multiscale feature pyramid fusion network utilizing convolutional neural network principles.
• Integration of shallow and deep network feature representations using first- and second-order characteristics.
• Incorporation of bottleneck and batch normalization layer modules with 1x1 convolutional kernels.

Main Results:

• The proposed network effectively extracts and combines features from multiple network layers, overcoming the limitations of single-layer approaches.
• Demonstrated potential for enhanced classification accuracy in evaluating international Chinese education quality.
• The architecture successfully integrates global and local discriminative region information.

Conclusions:

• The developed multiscale feature pyramid fusion network offers a promising approach for evaluating international Chinese education quality.
• This deep learning model can contribute to the standardization and improvement of Chinese language education worldwide.
• Further research can explore the application of this network in other educational assessment domains.