Big Data Methodology and Teaching Innovation of English Writing
Liuchun Wen
Guangdong Technical College of Water Resources and Electric Engineering, China
Keywords: Big Data Methodology, Teaching Innovation of English Writing, Million English Writing Contest.
Abstract: In the context of the era of big data, higher vocational English writing courses should make full use of Internet
technology and massive information data to build a new teaching model in terms of teaching concepts,
teaching forms, teaching resources, and teaching evaluation. Employing big data methodology and statistics
analysis, this paper explores the English compositions with the same topic for millions of students in 2016.
The adopted instruments include SPSS software, Matlab, SAS, Python software. Different graphs, pictures
and tables show the situation of students’ participating in the competition in each area, modification and score
change of students’ writing, and dimensional changes of students’ writing. The study shows that the
continuous development of information technology provides new auxiliary means and tools for writing
teaching. Big data brings opportunities and challenges to traditional English writing teaching.
1 INTRODUCTION
With the development and rapid popularization of
cloud computing technology, the Internet has entered
an era of rapid development. The Internet has
penetrated all aspects of human social activities. Due
to the rapid improvement in the ability of computers
to process data, people have discovered laws from the
seemingly disorganized massive data that could not
be found in the pre-Internet era. As a result, we have
entered the era of "big data". Under the background
of the era of big data, the concept and teaching form
of higher education have undergone profound
changes. The intellectualization and informatization
of English teaching reform is an important subject for
higher vocational English education researchers and
teachers (Chen, 2015). In the context of the era of big
data, higher vocational English writing courses
should make full use of Internet technology and
massive information data to build a new teaching
model in terms of teaching concepts, teaching forms,
teaching resources, and teaching evaluation. We will
use the big data method to study the English writing
of millions of the same topic in 2022 to construct an
English writing teaching model for higher vocational
education.
The basic characteristics of big data can be
summarized by five "Vs", namely Volume (large
capacity), Variety (many types), Velocity (fast
speed), Value (high value), Visualization
(visualization) (Eynon, 2013)
2 THE IMPACT OF BIG DATA
ON ENGLISH WRITING
The arrival of the era of big data will surely bring
about great changes in modern education. In the era
of big data, information-based education has become
one of its distinctive features. Faced with this new
situation, how to realize teaching innovation through
"technical delicacy" is a problem that every teacher
should seriously think about. English writing is an
important language skill, and big data has had a
profound impact on English writing. Wang Haixiao
believes that the era of big data is characterized by
college English writing teaching, including writing
teaching resources, writing purpose, writing content
and organization, and writing aids (Wang, 2014).
The connotation of tools, writing assessment and
writing ability has brought changes in many aspects
from concept to behavior, also brought new
opportunities and challenges to the reform of college
English writing teaching (Yang & Dai, 2015).
The continuous development of information
technology provides new auxiliary means and tools
for writing teaching. The research on intelligent essay
scoring system (automated essay scoring) at home
256
Wen, L.
Big Data Methodology and Teaching Innovation of English Writing.
DOI: 10.5220/0011909900003613
In Proceedings of the 2nd International Conference on New Media Development and Modernized Education (NMDME 2022), pages 256-260
ISBN: 978-989-758-630-9
Copyright
c
2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
and abroad has begun to take shape. Juku Correction
Network (hereinafter referred to as "Correction
Network") developed by Beijing Language
Intelligence Collaborative Research Institute is one of
the most influential online writing intelligent
platforms in China. The feature of the Correction
Network is that students submit their compositions
online, and the system immediately makes
assessment and feedback, and students can revise
their compositions unlimited times based on the
feedback. The automatic scoring system operates
quickly and accurately, saves a lot of labor, and
enhances immediacy and interactivity. In addition,
the intelligent automatic evaluation system realizes
the individualization of evaluation, can evaluate from
multiple dimensions such as vocabulary, grammar,
text and content, and provides various feedback
information for students' writing synchronously,
eliminating students' disgust for evaluation feedback.
Emotions and anxiety in the writing process can
effectively improve the efficiency of English writing
teaching and promote the improvement of students'
English writing ability.
In order to construct and mine the big data of
Chinese students' English writing, since 2014, the
Chinese College English Writing Teaching Alliance
and Beijing Language Intelligence Collaborative
Research Institute have carried out the "Millions of
English Writing Activities with the same topic" for 8
consecutive years. After 8 years of development, the
English writing activity on the same topic has
attracted more than 9 million teachers and students
from thousands of colleges and universities, and
provided a large amount of real corpus data for
Chinese English teaching and research. It has become
a well-known English teaching brand competition in
China.
To carry out research on writing corpus data, it is
necessary to master the methods and technologies of
big data. To this end, higher vocational English
teachers need to master various tools and software,
keep up with the development of modern information
technology, and learn the most advanced data
analysis and processing software, such as SPSS
software, Matlab, SAS, Python software, etc.
Especially worth mentioning is Python software,
which is an object-oriented interpreted computer
programming language, which is very useful in data
processing. Because it is a programming language,
many college teachers born in the liberal arts have a
deep sense of fear of it, but once mastered, it will
greatly help improve the ability to process
information.
3 EXPLORATION INTO THE
ENGLISH COMPOSITIONS
WITH THE SAME TOPIC FOR
MILLIONS OF STUDENTS IN
2016 BY WAY OF BIG DATA
METHODOLOGY
The 2016 English Writing Contest with the same
topic for millions of students is jointly sponsored by
the National Institute of Foreign Language Teaching
in Colleges and Universities and the China College
English Writing Teaching Alliance, and organized by
the Correction Network (www.pigai.org).
3.1 Situation
This topic is provided by Peking University. It
focuses on the impact of AI on human beings and
guides Chinese students to think about AI. From April
6 to May 31, 2016, 22532 teachers from 9384 schools
in 32 provinces and cities across the country
participated in the activity. The number of student
essays submitted reached 1739660, covering junior
high school (7.9%), high school (13.89%), higher
vocational (7.12%) and undergraduate (71.09%).
Table 1 shows The situation of students’ participating
in the competition in each area.
Table 1. The situation of students’ participating in the
competition in each area
Provinces/m
unicipalities
/autonomou
s regions
The
number of
compositi
ons
handed in
Provinces/m
unicipalities
/
autonomous
regions
The
number of
compositio
ns handed
in
Beijing 72,232 Chongqing 2,672
Sichuan 39,272 Tianjin 2,527
Shandong 23,694 Anhui 1,818
Guangdong 18,293 Liaoning 1,728
Jiangsu 11,246 Fujian 1,722
Shanxi 8,839 Guangxi 1,415
Jiangxi 5,793 Henan 1,318
Hebei 5,126 Yunnan 1,149
Zhejiang 3,065 Hubei 1,034
3.2 Modification and Score Change
As shown in Fig. 1, in this activity, the average
number of revisions of more than 200000 students'
compositions is 6.1, which means that each student
has revised more than 6 compositions on average with
Big Data Methodology and Teaching Innovation of English Writing
257
the help of machine correction under the condition of
completely independent learning during the winter
vacation.
After an average of 6.1 revisions per composition,
the original machine score of students' compositions
increased from 67.22 in the first edition to 70.48 in
the final edition, with an average increase of 3.26
points.
Figure 1. The change of the score of students’ composition in different editions
3.3 Dimensional Changes
Fig. 1 shows the change of the diction in different
editions. It can be seen that the measured values of
vocabulary richness, average word length and
average vocabulary difficulty have all improved,
which proves that students use more words in the final
version of the composition than in the first version,
and the difficulty of using words is slightly improved.
Figure 2. The change of the diction in different editions
Fig. 3 shows the change of the sentences in
different editions. It can be seen that the average
sentence length and clause density have slightly
increased, indicating that students constantly adjust
the rationality of their sentence structure, and use
clauses to make sentences in the text more abundant.
Figure 3. The change of the sentences in different editions
About improved 3.26 points on average
About 6.1 modification times on average
5.23
Diversity of word
choice in the final
version
4.27
Average length of words
in the final version
5.18
Average difficulty degree
of words in the final
version
5.12
Diversity of word
choice in the first
version
4.26
Average length of
words in the first
version
5.13
Average difficulty
degree of words in the
first version
15.46
Average length of
sentences
0.73
Density degree of
subordinate clause
15.67
Average length of
sentences
0.76
Density degree of
subordinate clause
The final version
The first version
NMDME 2022 - The International Conference on New Media Development and Modernized Education
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The following Table 2 shows the change of the
text in different editions. It can be seen that the length
of the article increases, and the number of
conjunctions students use increases, and students
noticed that the use of cohesive words in writing can
improve the coherence of the structure of the article,
the number of paragraphs increases, and students'
sense of segmentation increases.
Table 2. The change of the text in different editions
Index
Version
Length of
composition
The number
of
conjunctions
The
average
number of
p
ara
g
ra
p
hs
The final
version
172.41 11.38 3.19
The first
version
161.51 10.51 3.1
3.4 Big Data Method and Innovation in
English Writing Teaching
English writing teaching in the era of big data must
first change the educational concept and the role of
teachers (Wang, 2014). In traditional writing
classrooms, a teacher-centered teaching model based
on experience is often used. Teachers play a central
role in the teaching process, teaching writing skills
and assigning writing tasks. Students passively accept
knowledge and complete homework, and it is difficult
to get timely and targeted feedback in the teaching
process, so they cannot revise and improve their
writing in time. This kind of teaching mode is
monotonous, lacks pertinence, and cannot mobilize
students' interest and enthusiasm, so it is difficult to
achieve ideal teaching results. In the era of
information technology and big data, learning is more
of a self-organizing behavior of students. Students are
the center of learning activities and the main body of
the teaching process. Teachers are more likely to
provide guidance, support and services for learners.
With the rapid development of network technology,
network resources are readily available, and students
can obtain and utilize no less learning resources than
teachers. At this time, the main role of teachers is no
longer the transmitter of knowledge resources, but the
integrator of autonomous learning resources. The
teacher's responsibility is to adhere to the "student-
centered" educational philosophy, integrate excellent
learning resources on the Internet, make full use of
diverse resources and teaching methods, stimulate
students' continuous thinking, cultivate students'
interest in writing, guide students to learn
independently, and timely. As far as English writing
teaching is concerned, teachers’ role is to become an
integrator of writing learning resources, a digger of
student writing data, a designer of data-driven
precision teaching, and a professional assessor of
students' writing level. The data analysis of millions
of writing on the same topic can provide specific
guidelines for the design of precise teaching. On the
whole, Chinese students lack in-depth English
reading, as the proportion of students mentioning
literary works in the composition is only 1. 33%.
In traditional writing teaching, students "write for
the sake of writing" and "practice for the sake of
practice" are inevitably boring, students lack interest,
and it is inevitable that they will be perfunctory.
Students' writing for practice ignores the expressive
function of writing, which may cause students to pay
too much attention to grammar, vocabulary, structure
and other formal problems and ignore the core part of
the article, that is, content and ideas.
Network technology based on big data can help
solve the problem of writing purpose from two
aspects. On the one hand, writing software and online
writing systems can correct most of the formal errors,
freeing students to spend more time and energy to
conceive the content of the essay. On the other hand,
open web platforms make every writing a "share". For
example, teachers can let students write e-mails to
others, or share their comments or comments on the
Internet, or adopt the method of students’ mutual
evaluation of compositions, so that every writing has
a "reader", so that each student is both an author and
a reader. In this way, writing is no longer a dry
exercise, but a real transmission of information and
emotion, which returns to the most authentic purpose
of writing (Tang & Wu, 2012).
4 CONCLUSION
Big data brings opportunities and challenges to
traditional English writing teaching (Liu, 2014). How
teachers and students adjust their learning concepts
and methods in this context will determine the future
results of college English teaching. Teachers should
have a clear understanding of this trend, and should
actively participate in and adapt to this trend, big data
is used to analyze students' learning characteristics,
learning ability and learning motivation, so as to
make adjustments in teaching strategies and teaching
concepts, in order to achieve the best teaching effect.
Further research on English writing in the era of big
data involves not only college English teaching itself.
In addition, it can control and intervene in the
entire English learning process of students, such as
Big Data Methodology and Teaching Innovation of English Writing
259
primary school and middle school, to form an
integrated teaching of the entire English learning
process, optimally allocate learning resources, and
complete English learning goals.
ACKNOWLEDGEMENTS
2022 Guangdong Education Science Planning Project
(Higher Education Project) "Research on Higher
Vocational English Teaching Based on 'Output
oriented Approach' from the Perspective of National
Foreign Language Competence" (No.
2022GXJK506); The 2021 Guangdong Higher
Vocational Education Teaching Reform Research
and Practice Project "Exploration and Practice of the
Teaching Reform of the 'Comprehensive English'
Course for Higher Vocational English Majors under
the Theory of Curriculum Ideology and Politics".
REFERENCES
Chen Jianlin. Research on MOOC and foreign language
teaching in the era of big data [J]. Foreign language
Electronic Teaching, 2015(1).
Eynon, R. The rise of Big Data: What does it mean for
education, technology and media research[J]. Learning,
Media and Technology, 2013(3).
Gao Yujuan, Zhao Xiaodong. Corpus-based English majors
in China Quantitative analysis of the use of adjectives
and adjectives [J]. Foreign Language Studies, 2020,
37(2): 24 - 31.
Hu Xuewen. The influence of online composition self-
correction on college students' English writing results
[J]. Foreign Language Electronic Teaching, 2015(3).
Liu Runqing. Foreign language education and scientific
research in the era of big data [J]. contemporary foreign
language studies Research, 2014 (7).
Tang Jinlan, Wu Yi'an. Writing automatic evaluation
system in college English teaching
research on the application of Chinese language [J]. Foreign
Language and Foreign Language Teaching, 2012 (4):
53-59.
Victor Meier-Schoenberg, Kenneth Cooke. when big data
Generation: The Great Change of Life, Work and
Thinking [M]. Hangzhou: The People of Zhejiang
Press, 2013.
Wang Haixiao. The reform of college English writing
teaching in the era of big data [J]. Modern Online
Education Research, 2014(3): 66-72.
Wang Tsunami. College English writing teaching reform in
the era of big data [J]. Modern Distance Education
Research, 2014(3).
Wang Ying, Li Zhen Yang. A Research Review on
Electronic Feedback Models in Foreign Second
Language Writing[J]. Foreign Language Teaching,
2012 (4): 11-16.
Wang Zhe, Li Junjun. Exploration on the reform of foreign
language general education in colleges [J]. foreign
language Chemical Teaching, 2010(5).
Yang Xianmin. The connotation and characteristics of
smart education in the information age [J]. China
Electronic Education, 2014, 33(1): 29-34.
Yang Xiaoqiong, Dai Yuncai. College English
Autonomous Writing Teaching Based on Correction
Net Model practice research [J]. Foreign Language
Electronic Teaching, 2015(2).
Yang Yonglin. English teaching in the era of globalization,
informatization and digitalization Learning—a research
on the construction of training system based on
“experience English writing” [J]. Foreign Language
and Foreign Language Teaching, 2008(5).
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