Constructing a Data-Driven Model of English Language Teaching with a Multidimensional Corpus
In this study, a multidimensional corpus English teaching model is constructed using the data-driven model. This study uses a data-driven collection of massive amounts of data to generate a multidimensional corpus. The data-driven generation of a multidimensional corpus to build a teaching model is...
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2-s2.0-85133960698 Chen D. Constructing a Data-Driven Model of English Language Teaching with a Multidimensional Corpus 2022 Mathematical Problems in Engineering 2022 10.1155/2022/2715408 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133960698&doi=10.1155%2f2022%2f2715408&partnerID=40&md5=e00a03b04847d56be4de095b14a5218a In this study, a multidimensional corpus English teaching model is constructed using the data-driven model. This study uses a data-driven collection of massive amounts of data to generate a multidimensional corpus. The data-driven generation of a multidimensional corpus to build a teaching model is studied, and the principle of data driven, the computational process, and the characteristics of the corpus are analyzed. Due to the deficiency of data-driven modeling without correlating process variables with quality variables, this study adopts an artificial intelligence algorithm and analyzes the basic principle, computational process, and advantages and disadvantages of the method. The model is simulated and verified for multidimensional corpus and English teaching. To address the shortcomings of the AI algorithm, which has a complex computation process and no orthogonal decomposition of the data space, the autoregressive latent structure projection algorithm is designed by integrating the autoregressive idea with the artificial intelligence (AI) algorithm. This algorithm can orthogonally decompose the sample data space and simplify the modeling process. Finally, the algorithm is validated by simulation. To verify the results of the teaching model, the fuzzy C-means clustering algorithm is combined with the autoregressive latent structure projection algorithm in this study. The sample data used in the modeling are divided into categories, and the affiliation function is calculated for each category. The affiliation function is used to calculate the affiliation of the online calculation results for each category, and the final evaluation results are obtained based on the fuzzy comprehensive evaluation method. Finally, taking junior students as an example, the simulation is carried out to verify the effectiveness of the English teaching model. The research results show that the corpus-based English flipped classroom teaching model improves English teaching methods, enhances students' English proficiency and independent learning ability, and provides a practical basis for English teaching model exploration. © 2022 Dongyan Chen. Hindawi Limited 1024123X English Article All Open Access; Gold Open Access |
author |
Chen D. |
spellingShingle |
Chen D. Constructing a Data-Driven Model of English Language Teaching with a Multidimensional Corpus |
author_facet |
Chen D. |
author_sort |
Chen D. |
title |
Constructing a Data-Driven Model of English Language Teaching with a Multidimensional Corpus |
title_short |
Constructing a Data-Driven Model of English Language Teaching with a Multidimensional Corpus |
title_full |
Constructing a Data-Driven Model of English Language Teaching with a Multidimensional Corpus |
title_fullStr |
Constructing a Data-Driven Model of English Language Teaching with a Multidimensional Corpus |
title_full_unstemmed |
Constructing a Data-Driven Model of English Language Teaching with a Multidimensional Corpus |
title_sort |
Constructing a Data-Driven Model of English Language Teaching with a Multidimensional Corpus |
publishDate |
2022 |
container_title |
Mathematical Problems in Engineering |
container_volume |
2022 |
container_issue |
|
doi_str_mv |
10.1155/2022/2715408 |
url |
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133960698&doi=10.1155%2f2022%2f2715408&partnerID=40&md5=e00a03b04847d56be4de095b14a5218a |
description |
In this study, a multidimensional corpus English teaching model is constructed using the data-driven model. This study uses a data-driven collection of massive amounts of data to generate a multidimensional corpus. The data-driven generation of a multidimensional corpus to build a teaching model is studied, and the principle of data driven, the computational process, and the characteristics of the corpus are analyzed. Due to the deficiency of data-driven modeling without correlating process variables with quality variables, this study adopts an artificial intelligence algorithm and analyzes the basic principle, computational process, and advantages and disadvantages of the method. The model is simulated and verified for multidimensional corpus and English teaching. To address the shortcomings of the AI algorithm, which has a complex computation process and no orthogonal decomposition of the data space, the autoregressive latent structure projection algorithm is designed by integrating the autoregressive idea with the artificial intelligence (AI) algorithm. This algorithm can orthogonally decompose the sample data space and simplify the modeling process. Finally, the algorithm is validated by simulation. To verify the results of the teaching model, the fuzzy C-means clustering algorithm is combined with the autoregressive latent structure projection algorithm in this study. The sample data used in the modeling are divided into categories, and the affiliation function is calculated for each category. The affiliation function is used to calculate the affiliation of the online calculation results for each category, and the final evaluation results are obtained based on the fuzzy comprehensive evaluation method. Finally, taking junior students as an example, the simulation is carried out to verify the effectiveness of the English teaching model. The research results show that the corpus-based English flipped classroom teaching model improves English teaching methods, enhances students' English proficiency and independent learning ability, and provides a practical basis for English teaching model exploration. © 2022 Dongyan Chen. |
publisher |
Hindawi Limited |
issn |
1024123X |
language |
English |
format |
Article |
accesstype |
All Open Access; Gold Open Access |
record_format |
scopus |
collection |
Scopus |
_version_ |
1818940560053895168 |