Assessing Digital Teaching Competence: An Approach for International Chinese Teachers Based on Deep Learning Algorithms

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Liqing Yang
Qicheng Wang
Borui Zheng
Xuan Li
Xitong Ma
Tianyu Wang


Digital Teaching Competency (DTC) is an important skill in the professional development of international Chinese language teachers. This study developed a new deep learning-based assessment model of DTC for international Chinese language teachers. To build this model, the researchers first collected data on DTC from 221 international Chinese language teachers at different levels in 26 countries to ensure that these sample data are representative; secondly, clustering and feature dimensionality reduction techniques were used to preprocess the data and constructed the Siamese architectural model; and finally, the researchers confirmed through experimental validation and expert evaluations that the model has a high accuracy rate of 96.33%. The innovation of this model is to use the traditional three-level network as an improved constructed digital twin network, so as to extract some features that are more accurate and to characterize those features that are most predictive. The improved network is able to extract all the inputs globally and also locally that are of most interest to the user/researcher, the final prediction results are weighted, and those weighted results are used as the final prediction output of the model. This model not only provides systematic and adaptive support for improving teachers' DTC, but through the comprehensive result output, it can provide targeted improvement strategies for teachers to improve their DTC.


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Special Issue - Scalable Computing in Online and Blended Learning Environments: Challenges and Solutions