Applying digital natural language processing tools to optimize subject teacher training in a multilingual educational environment

Abstract

The aim of the research is to automate the analysis process of the most common grammatical forms and syntactic structures of a foreign or second language to optimize the content and acquisition of English language programs in the training of future Mathematics teachers for professional activities in a multilingual educational environment. The paper theoretically justifies the necessity of a comprehensive frequency analysis of language structures. Software tools for natural language processing (the Python programming language and the spaCy library) were developed, which were subsequently applied in the course “Methods of Mathematical Data Processing” to identify the most frequent grammatical forms and syntactic structures characteristic of the English language of Mathematics. Based on the data obtained, methodological recommendations were formulated for the content and outcomes of English language programs for future Mathematics teachers in a multilingual educational environment. The scientific novelty of the research lies in using digital natural language processing (NLP) tools for a quantitative and statistical analysis of language data, necessary for the development of methodological recommendations for the content and outcomes of foreign/second language programs for subject teacher training. As a result of the research, it was found that digital natural language processing tools can be used to improve the language training of subject teachers in a multilingual educational environment.

References

1. Артемов В. А. Психология обучения иностранным языкам. М.: Просвещение, 1969.
2. Берман И. М. Методика обучения английскому языку в неязыковых вузах. М.: Высшая школа, 1970.
3. Зарипова Р. Р., Данилов А. В., Салехова Л. Л. Трансформация полилингвального педагогического образования посредством цифровых образовательных ресурсов: кейс Республики Татарстан // Концепт. 2023. № 5 (май).
4. Зарипова Р. Р., Салехова Л. Л. Об особенностях овладения математикой и иностранным языком: сопоставительный анализ // Современные проблемы науки и образования. 2012. № 6.
5. Миролюбов А. А. К вопросу об отборе грамматического минимума для средней школы // Иностранные языки в школе. 1956. № 2.
6. Рахманов И. В. Обучение устной речи на иностранном языке. М.: Высшая школа, 1980.
7. Bird S., Klein E., Loper E. Natural Language Processing with Python. Sebastopol: O’Reilly Media, 2009.
8. Gatiyatullina G., Solnyshkina M., Solovyev V., Danilov A., Martynova E., Yarmakeev I.Computing Russian Morphological Distribution Patterns Using RusAC Online Server // 13th International Conference on Developments in eSystems Engineering (DeSE). Liverpool, 2020.
9. Řehůřek R., Sojka P. Software Framework for Topic Modelling with Large Corpora // Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Valletta, 2010.
10. Salekhova L., Danilov A., Zaripova R., Ilminbetova S. Effective Use of Video Lectures: Guidelines and Criteria // 14th International Technology, Education and Development Conference (INTED2020). Valencia, 2020.

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About this article

Publication history

• Received: December 16, 2023.
• Published: January 26, 2024.

Keywords

• полилингвальное образование
• русский язык
• английский язык
• обработка естественного языка
• технология предметно-языкового интегрированного обучения
• multilingual education
• Russian language
• English language
• natural language processing
• Content and Language Integrated Learning (CLIL)

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