<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>At the end of the Qing Dynasty, educational reforms precipitated the proliferation of new-style schools, thereby diminishing the prominence of traditional Confucian textbooks in their curricula. This study investigates the pedagogical approaches of Chinese education during the late Qing and early Republic of China periods, focusing on the educational reform movement and the shift from traditional to modern school systems. Utilizing the Scrapy crawling framework, the research involved collecting educational content from 1900 to 1940 to establish a comprehensive corpus. Subsequently, the Word2Vec machine learning algorithm was employed to derive word vectors from this corpus. At the same time, a hybrid model, HS-BTM, which integrates heat, semantic information, and the Biterm Topic Model (BTM), was developed to facilitate a nuanced analysis of semantic shifts in educational content. This analysis was conducted along two dimensions: morphological word semantics and components of teaching content. The findings reveal that the teaching materials contained 702 sets of compound anagrams, and the chemistry textbooks from the period under review featured a significant emphasis on everyday life themes, accounting for 53.25% of the content. By leveraging machine learning techniques, this study effectively charts the evolution of Chinese educational practices and offers valuable insights for the ongoing development of educational strategies.</p>
</abstract>

At the end of the Qing Dynasty, educational reforms precipitated the proliferation of new-style schools, thereby diminishing the prominence of traditional Confucian textbooks in their curricula. This study investigates the pedagogical approaches of Chinese education during the late Qing and early Republic of China periods, focusing on the educational reform movement and the shift from traditional to modern school systems. Utilizing the Scrapy crawling framework, the research involved collecting educational content from 1900 to 1940 to establish a comprehensive corpus. Subsequently, the Word2Vec machine learning algorithm was employed to derive word vectors from this corpus. At the same time, a hybrid model, HS-BTM, which integrates heat, semantic information, and the Biterm Topic Model (BTM), was developed to facilitate a nuanced analysis of semantic shifts in educational content. This analysis was conducted along two dimensions: morphological word semantics and components of teaching content. The findings reveal that the teaching materials contained 702 sets of compound anagrams, and the chemistry textbooks from the period under review featured a significant emphasis on everyday life themes, accounting for 53.25% of the content. By leveraging machine learning techniques, this study effectively charts the evolution of Chinese educational practices and offers valuable insights for the ongoing development of educational strategies.

The Integration of Chinese Educational Teaching and Machine Learning Technologies in Cross-Cultural Exchange during the Late Qing and Early Republican Periods

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

At the end of the Qing Dynasty, educational reforms precipitated the proliferation of new-style schools, thereby diminishing the prominence of traditional...

The Integration of Chinese Educational Teaching and Machine Learning Technologies in Cross-Cultural Exchange during the Late Qing and Early Republican Periods

Publicado en línea: 03 jun 2024

Páginas: -

Aceptado: 01 may 2024

DOI: https://doi.org/10.2478/amns-2024-1354

Palabras clave
Scrapy crawler framework, Machine learning techniques, Word2Vec language model, Corpus, HS-BTM topic model, Chinese education

© 2024 Xue Fang, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The Integration of Chinese Educational Teaching and Machine Learning Technologies in Cross-Cultural Exchange during the Late Qing and Early Republican Periods

Publicado en línea: 03 jun 2024

Páginas: -

Aceptado: 01 may 2024

DOI: https://doi.org/10.2478/amns-2024-1354

Palabras claveScrapy crawler framework, Machine learning techniques, Word2Vec language model, Corpus, HS-BTM topic model, Chinese education

© 2024 Xue Fang, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Scrapy crawler framework, Machine learning techniques, Word2Vec language model, Corpus, HS-BTM topic model, Chinese education