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从多维环境分析英语语言文学对学生语言能力的影响。

An Analysis of the Effects of the English Language and Literature on Students' Language Ability from a Multidimensional Environment.

机构信息

Department of Public Basic Courses, Hunan Environment-Biological Polytechnic, Hengyang 421005, China.

出版信息

J Environ Public Health. 2022 Sep 13;2022:3225096. doi: 10.1155/2022/3225096. eCollection 2022.

DOI:10.1155/2022/3225096
PMID:36148405
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9489394/
Abstract

One of the most crucial components of a student's language proficiency is basic language proficiency, which is also its fundamental component. The development of students' language skills is greatly aided by ELL (English Language and Literature). It can not only foster the growth of students' language thinking but also widen their perspectives and enhance their capacity for language comprehension. In this essay, the rules of English are examined from the multifaceted ELL viewpoint. This study extracts personality characteristic data from practical texts and incorporates it into a modelling process of students' knowledge changes based on DM- (data mining-) related technology and multidisciplinary expertise. According to the -test results, ELL, which incorporates communicative competence, has started to take on a role in education and is helping students' language skills. The performance gains are 3.95, 4.21, and 3.66, when the number of computing nodes is increased to 10, 20, and 30 times that of the original nodes, and the linear expansion indices are 0.914, 0.928, and 0.93. This proves that the Hadoop+Hive-based data cleaning tool has excellent linear scalability.

摘要

学生语言能力的一个最关键的组成部分是基本语言能力，这也是其基本组成部分。ELL（英语语言和文学）极大地促进了学生语言技能的发展。它不仅可以培养学生的语言思维，还可以拓宽他们的视野，提高他们的语言理解能力。在这篇文章中，从多方面的 ELL 观点来研究英语规则。本研究从实际文本中提取个性特征数据，并将其纳入基于 DM-（数据挖掘-）相关技术和多学科专业知识的学生知识变化建模过程中。根据测试结果，包含交际能力的 ELL 已经开始在教育中发挥作用，并帮助学生提高语言技能。当计算节点的数量增加到原始节点的 10、20 和 30 倍时，性能增益分别为 3.95、4.21 和 3.66，线性扩展指数分别为 0.914、0.928 和 0.93。这证明了基于 Hadoop+Hive 的数据清理工具具有出色的线性可扩展性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/d1bd1780ca47/JEPH2022-3225096.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/c35ff848bbe2/JEPH2022-3225096.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/40132bc8b56f/JEPH2022-3225096.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/16b7ace98750/JEPH2022-3225096.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/87d586756ff6/JEPH2022-3225096.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/c9e8d0ddf999/JEPH2022-3225096.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/8fa198f804f9/JEPH2022-3225096.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/d1bd1780ca47/JEPH2022-3225096.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/c35ff848bbe2/JEPH2022-3225096.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/40132bc8b56f/JEPH2022-3225096.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/16b7ace98750/JEPH2022-3225096.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/87d586756ff6/JEPH2022-3225096.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/c9e8d0ddf999/JEPH2022-3225096.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/8fa198f804f9/JEPH2022-3225096.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b054/9489394/d1bd1780ca47/JEPH2022-3225096.007.jpg

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