An English Network Teaching Method Supported by Artificial Intelligence Technology and WBIETS System

被引：21

作者：

Du, Haibao ^{[1
]}

机构：

[1] Shenyang Normal Univ, Teaching Sect Foreign Languages, Shenyang, Peoples R China

来源：

SCIENTIFIC PROGRAMMING | 2021年 / 2021卷

关键词：

MACHINE LEARNING-METHODS; PREDICTION; MODELS;

D O I：

10.1155/2021/8783899

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The traditional English teaching system has certain problems in the acquisition of teaching resources and the innovation of teaching models. In order to improve the effect of subsequent English online teaching, this paper improves the machine learning algorithm to make it a core algorithm that can be used by artificial intelligence systems. Moreover, this paper combines the WBIETS system to expand the system function, analyzes the needs of the English network teaching system, and constructs the system function modules and logical structure. The data layer, logic layer, and presentation layer in the system constructed in this paper are independent of each other and can be effectively expanded when subsequent requirements change. In addition, this paper solves the problem of acquiring English teaching resources through the WBIETS system. To evaluate the performance of the English network teaching system, this paper performs comprehensive mathematical and experimental analysis. The experimental results show that the system constructed in this paper basically meets the actual teaching requirements.

引用

页数：9

共 22 条

[1] Power to the People: The Role of Humans in Interactive Machine Learning [J].

Amershi, Saleema ;

Cakmak, Maya ;

Knox, W. Bradley ;

Kulesza, Todd .

AI MAGAZINE, 2014, 35 (04) :105-120

[2] SDM6A: A Web-Based Integrative Machine-Learning Framework for Predicting 6mA Sites in the Rice Genome [J].

Basith, Shaherin ;

Manavalan, Balachandran ;

Shin, Tae Hwan ;

Lee, Gwang .

MOLECULAR THERAPY-NUCLEIC ACIDS, 2019, 18 :131-141

[3] Machine Learning for Precision Psychiatry: Opportunities and Challenges [J].

Bzdok, Danilo ;

Meyer-Lindenberg, Andreas .

BIOLOGICAL PSYCHIATRY-COGNITIVE NEUROSCIENCE AND NEUROIMAGING, 2018, 3 (03) :223-230

[4] Feature selection in machine learning: A new perspective [J].

Cai, Jie ;

Luo, Jiawei ;

Wang, Shulin ;

Yang, Sheng .

NEUROCOMPUTING, 2018, 300 :70-79

[5] Disease Prediction by Machine Learning Over Big Data From Healthcare Communities [J].

Chen, Min ;

Hao, Yixue ;

Hwang, Kai ;

Wang, Lu ;

Wang, Lin .

IEEE ACCESS, 2017, 5 :8869-8879

[6] Image driven machine learning methods for microstructure recognition [J].

Chowdhury, Aritra ;

Kautz, Elizabeth ;

Yener, Bulent ;

Lewis, Daniel .

COMPUTATIONAL MATERIALS SCIENCE, 2016, 123 :176-187

[7] Prediction of Organic Reaction Outcomes Using Machine Learning [J].

Coley, Connor W. ;

Barzilay, Regina ;

Jaakkola, Tommi S. ;

Green, William H. ;

Jensen, Klays F. .

ACS CENTRAL SCIENCE, 2017, 3 (05) :434-443

[8] Urban flood risk mapping using the GARP and QUEST models: A comparative study of machine learning techniques [J].

Darabi, Hamid ;

Choubin, Bahram ;

Rahmati, Omid ;

Haghighi, Ali Torabi ;

Pradhan, Biswajeet ;

Klove, Bjorn .

JOURNAL OF HYDROLOGY, 2019, 569 :142-154

[9] Incorporating machine learning with biophysical model can improve the evaluation of climate extremes impacts on wheat yield in south-eastern Australia [J].

Feng, Puyu ;

Wang, Bin ;

Liu, De Li ;

Waters, Cathy ;

Yu, Qiang .

AGRICULTURAL AND FOREST METEOROLOGY, 2019, 275 :100-113

[10] Spatio-temporal downscaling of gridded crop model yield estimates based on machine learning [J].

Folberth, C. ;

Baklanov, A. ;

Balkovic, J. ;

Skalsky, R. ;

Khabarov, N. ;

Obersteiner, M. .

AGRICULTURAL AND FOREST METEOROLOGY, 2019, 264 :1-15

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