Evaluation of English Proficiency Based on Big Data Clustering Algorithm

As one of the common languages in the world, English is playing a more and more important role in trade exchanges, cultural exchanges, and other transnational cooperation. In order to better integrate into the world and communicate with the world, China began to vigorously expand English learning as...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Wireless communications and mobile computing Ročník 2022; číslo 1
Hlavný autor: Duan, Li
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Oxford Hindawi 2022
John Wiley & Sons, Inc
Predmet:
Algorithms
Big Data
Clustering
Education
Evaluation
Fuzzy logic
Globalization
Grammar
Information processing
Intelligence
Knowledge
Learning
Neural networks
Reading
Students
Teachers
Teaching
Union of Soviet Socialist Republics > USSR
China
ISSN:1530-8669, 1530-8677
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:As one of the common languages in the world, English is playing a more and more important role in trade exchanges, cultural exchanges, and other transnational cooperation. In order to better integrate into the world and communicate with the world, China began to vigorously expand English learning as early as the late 1980s. However, with the development trend of economic globalization and cultural diversity, the traditional English ability evaluation methods are not enough to comprehensively measure individual English ability. Therefore, based on this background, this paper introduces the clustering algorithm into English ability evaluation, improves the traditional clustering algorithm represented by K-means on the basis of predecessors, and captures the evaluation data through neural network algorithm combined with the background of modern big data framework. The results show that the fuzzy logic feature mapping clustering algorithm (FLFM) proposed in this paper performs better than the traditional evaluation algorithm, and the evaluation effectiveness and comprehensiveness are improved by about 10 pt.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1530-8669
1530-8677
DOI:10.1155/2022/5718681