数据挖掘技术助力高校英语专业数字化教学

You Li, Fengmei Shang
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引用次数: 0

摘要

摘要信息技术在高校英语专业教学中的应用丰富了教学手段,尤其是数据挖掘技术优化了数字化教学方法。本文从英语写作出发,基于写作训练的功能需求,构建了一个写作训练流程,并利用NMF方法对英语新闻文本主题进行挖掘和分解。结合写作训练,采用以Rete算法为核心的数据挖掘技术,在自然语言处理的基础上对英语作文进行自动诊断。在挖掘和分析英语新闻文本的基础上,比较英语写作评分的效果,并通过对照测试验证其对英语作文的增强作用。实验组学生写作平均分19.62分,满分25分。对照组的写作平均分为16.38分,比实验组低3.24分。显著性Sig =0.0004<0.05, P=0.025<0.05,两组学生的英语水平有显著性差异。数据挖掘技术提高了学生英语写作的语言能力。
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Data Mining Technology Helps Digital Teaching and Learning of English Majors in Colleges and Universities
Abstract The use of information technology in the teaching of English majors in colleges and universities enriches the teaching means, especially the data mining technology that optimizes the digital teaching method. In this paper, starting from English writing, based on the functional requirements of writing training, we constructed a writing training process and utilized the NMF method to mine and decompose the English news text topics. In conjunction with the writing training, the data mining technology centered on the Rete algorithm was used to carry out automatic diagnosis of English composition on the basis of natural language processing. On the basis of mining and analyzing the English news text, the effect of English writing scoring was compared, and its enhancement effect on English composition was verified through controlled tests. The average writing score of the experimental group was 19.62, of which the full score was 25. The mean writing score of the control group is 16.38, which is 3.24 points lower than that of the experimental group. Significance Sig.=0.0004<0.05, P=0.025<0.05, there is a significant difference in the English proficiency of the two groups of students. Data mining technology enhances the language proficiency of students in English writing.
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来源期刊
Applied Mathematics and Nonlinear Sciences
Applied Mathematics and Nonlinear Sciences Engineering-Engineering (miscellaneous)
CiteScore
2.90
自引率
25.80%
发文量
203
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