التفاصيل البيبلوغرافية
| العنوان: |
ASSESSING DIGITAL TEACHING COMPETENCE: AN APPROACH FOR INTERNATIONAL CHINESE TEACHERS BASED ON DEEP LEARNING ALGORITHMS. |
| المؤلفون: |
LIQING YANG, QICHENG WANG, BORUI ZHENG, XUAN LI, XITONG MA, TIANYU WANG |
| المصدر: |
Scalable Computing: Practice & Experience; Jan2024, Vol. 25 Issue 1, p495-509, 15p |
| مصطلحات موضوعية: |
MACHINE learning, DEEP learning, CAREER development, LANGUAGE teachers, TEACHER development, CHINESE language |
| مستخلص: |
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. [ABSTRACT FROM AUTHOR] |
|
Copyright of Scalable Computing: Practice & Experience is the property of Scalable Computing: Practice & Experience and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
