Research ArticleImprovement of English Teaching Model Based onComputer Network

Yining Du

College of Foreign Languages, Xi’an University of Finance and Economics, Xi’an, 710100 Shaanxi, China

Correspondence should be addressed to Yining Du; yining_du@xaufe.edu.cn

Received 13 July 2021; Revised 13 August 2021; Accepted 23 August 2021; Published 17 September 2021

Academic Editor: Haibin Lv

Copyright © 2021 Yining Du. This is an open access article distributed under the Creative Commons Attribution License, whichpermits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

The increase in the number of colleges and universities has brought great pressure to college English teaching. How to improve theteaching efficiency of English mode is an urgent problem to be solved. This article mainly studies the improvement of Englishteaching mode based on computer image assistance. In the preexperiment test, a unified English final test paper was selected,and the scores of 40 students were entered into the computer. The experimental group chose the English teaching mode ofcomputer-assisted teaching. The control group still used traditional teaching methods to learn. At the end of the experiment,the students in the experimental group and the control group were given a posttest of writing in their respective classrooms,and the teachers gave their scores according to the scoring standard of the college entrance examination. SPSS 17.0 software isused to analyze the students’ scores, and the experimental results are obtained by comparing the changes of students’ scoresbefore and after the test. In groups, tasks are done together, but it also helps students develop a correct sense of collectiveresponsibility and fair play. Students can evaluate team members from the aspects of activity enthusiasm and learningparticipation. Students’ self-assessment can be evaluated objectively by observing their preclass learning level, chapter taskcompletion, class attendance, group cooperation participation, and so on. In terms of learning motivation, the average value ofstudents’ online learning initiative consciousness (3.04) and the average value of learning interest (3.05) are slightly higher than3. The results show that computer image-assisted teaching can maximize the use of classrooms to improve students’opportunities to use language and fully reflect students’ role as the main body of practice in English learning.

1. Introduction

The network is changing the teaching methods of teachers.Nowadays, teachers can teach for thousands of students bythemselves. By using the interactivity of the network,teachers and students can interact in time, so as to achievethe similar effect with small class teaching. At the same time,people can enjoy high-quality online courses at home. Aslong as there is a computer, anyone can receive universityeducation anywhere. When multimedia and Internet areused in English teaching, first of all, students can learnautonomously and individually according to their own inter-ests, needs, tasks, and cognitive styles, thus breaking throughthe space-time limitation of traditional teaching mode andconstructing an unlimited open teaching space.

China’s English teaching has been highly valued byschools and families. From primary school to university,

English always occupies the position of the core curriculum,but the effect of English teaching is not satisfactory. By usingcomputer-assisted instruction in the first mock exam, we canexplore whether this mode can change students’ attitudetowards learning English and improve their motivationand motivation to participate in the classroom. On this basis,we can improve students’ English level and put forwardpractical teaching suggestions for current English teaching.

Computer image-assisted teaching has greatly promotedthe improvement of English teaching mode. Gamagebelieves that although GTM is a traditional teaching model,it has been widely used in second language teaching, espe-cially in higher education. Although there is no conclusiveevidence that GTM was originally built on a specific theoret-ical framework, its main translation and memory technolo-gies still play a vital role in the process of second languageteaching. His research was part of an action research

HindawiJournal of SensorsVolume 2021, Article ID 4756277, 10 pageshttps://doi.org/10.1155/2021/4756277

conducted at the Buddhist and Pali University in Sri Lanka,and 60 students were purposefully selected as samples. Theessential research adopts a pretest and posttest experimentalresearch design. He used a paired-sample t-test to comparethe difference between the mean values: a significant p value(p < 0:005) indicates the effectiveness of GTM in teachingapplications. Although his research can be effectively inte-grated into current teaching practice, its accuracy is insuffi-cient [1]. Moudden focuses on the integration of games inforeign language teaching, aiming to explore the impact ofthe use of games in the classroom on the process of foreignlanguage learning, students’ cognition of game use, students’game skills, students’ participation in games, the impact oflearning English, and their attitude. The subject of the ques-tionnaire survey was a group of foreign students studying atthe University of Letters and Humanities at the University ofMeknes More Ismail, Morocco. The sample included 50male and female participants. Although his research canimprove teachers’ understanding of the importance of gameintegration in foreign language teaching, his experimentalprocess is incomplete [2]. Sidash et al.’s research is dedicatedto scientifically demonstrating the formation of educationalawareness of university educators in the process of Englishteaching and determining the level of formation of their edu-cational awareness. His research reveals the organizationaland methodological conditions for the formation of educa-tional awareness of educators in the process of collegeEnglish teaching. He defined the effectiveness and optimiza-tion criteria (content and activity, motivation and value, andevaluation and reflection) of the formation of educationalawareness. Although his research is relatively comprehen-sive, it lacks innovation [3]. The main purpose of Sharmaand Khanal’s research is to explore the effectiveness of theteaching methods of English rhetoric. They selected 31 fig-ures of speech as research objects. They adopted a pretestand posttest control group research design to study 120third-year undergraduate students from five campuses inMakawanpur District, Nepal. They used lecture teachingmethod and discussion teaching method, respectively, for35 days of teaching. The SPSS 20th edition paired-sample t-test was used to compare the total scores of the pretestand posttest of each group. Although their research methodsare relatively complete, there is a lack of discussion of exper-imental results [4].

The innovation of this article is to use computer-assistedteaching technology to present the content to be learned tostudents in a multimodal way so that students can learneffectively through various senses and with more resources,which improves teachers’ teaching in class. The efficiency ofknowledge promotes the ability of students to absorbknowledge and greatly changes the limitations of traditionalteaching models. After the integration of computer networkand foreign language teaching, the teaching mode has chan-ged and the traditional teacher-centered mode has beenbroken. Foreign language teaching not only emphasizesthe student-centered teaching mode but also emphasizesthe information-based teaching mode of using moderninformation technology to promote students’ autonomouslearning.

2. Computer Image-Assisted Teaching andEnglish Teaching Mode

2.1. Computer Graphics-Assisted Teaching. The teachingprocess of English is quite different from that of other sub-jects, most of the teaching materials used in the currentcollege English courses are mainly competitive English, andmost of the contents are coherent dynamic technical move-ments. In the traditional way of college English teaching, itis mainly for college English teachers to personally demon-strate and explain the relevant skills and techniques. At thesame time, this traditional teaching method also seriouslylimits the play of college English teachers [5, 6].

If the current advanced teaching technology is used tosynthesize and decompose each technical action, it will bedifferent. Using the combination of CAI courseware produc-tion technology and network technology can make the stan-dardization of oral English in English teaching and the effectof unified and standardized teaching will be greatlyimproved [7]. If you continue to use the traditional andbackward English teaching methods of college Englishteaching, it will be difficult to adapt to the current rapidlyupdated environment of knowledge and information, andit will be impossible to reconcile the valuable informationand new theories in the field of English teaching in Englishteaching. The method is taught to students in time [8]. Asan important part of the education system, college Englishteaching has not only the commonalities with other disci-plines but also its own different characteristics. The Pearsoncorrelation coefficient between two variables is defined as thequotient of the covariance and standard deviation betweenthe two variables [9]:

ρX,Y =cov X, Yð Þ

σXσY=E X − μXð Þ Y − μYð Þ½ �

σXσY: ð1Þ

Among them, μX represents the mean value of variableX, σX represents the standard deviation of variable X, μYrepresents the mean value of variable Y , and σY representsthe standard deviation of variable Y [10]. Specifically, thegreater the absolute value of the Pearson product-momentcorrelation coefficient, the better the linear correlationbetween the subjective scoring results and the objective pre-diction results. On the contrary, it shows that there is nogood linear correlation between the stereo scoring resultsand the objective prediction results [11].

For a sample with a sample size of N , N original data Xand Y are converted into grade data x and y, and the Spear-man grade correlation coefficient is expressed as [12]

ρ =∑i xi − �xð Þ yi − �yð Þffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi∑i xi − �xð Þ2∑i yi − �yð Þ2

q : ð2Þ

Among them, �x represents the average value of rank datax, and �y represents the average value of rank data y [13].

Based on the training sample data set to construct thecorresponding regression function f ðXk′Þ of Xk′, the calcula-tion formula is as follows [14]:

2 Journal of Sensors

f Xk′� �

= 〠l

′=1qwTk Xk′, Xl′� �

+ b,

k Xk′, Xl′� �

= exp −Xk′ − Xl′�� ��2

γ2

!:

ð3Þ

wT is the transposition matrix of the weight vector, b is theoffset, and kðXk′, Xl′Þ is the radial basis kernel function [15].

Test and analyze each stereo image/video in the test sam-ple data set, and predict the corresponding objective predic-tion evaluation result Q [16].

Qi = f Xið Þ = 〠t

j=1woptk Xi, Xj

� �+ bopt, ð4Þ

where t represents the number of stereo image/videopairs contained in the test sample data, that is, (M-Q) pairs.Xi represents the combination of the i-th pair of perceptualfeature vectors of the stereo image/video content of the testsample data [17].

Suppose there are two spatial scenes A and B each con-taining n spatial targets. At the same time, considering thesimilarity and relationship of the targets and also consider-ing the importance of different spatial features to differentscenarios, the expression is as follows [18]:

SIM = KG × SIMG A, Bð Þ + KR × SIMR A, Bð Þ: ð5Þ

Among them, KG is the weight of target similarity;SIMGðA, BÞ is the target similarity of the two scenes; KR isthe weight of the relationship similarity; SIMRðA, BÞ is thesimilarity of the relationship between the two scenes [19].

SIMG A, Bð Þ = KS × SIMS A, Bð Þ + Kθ × SIMθ A, Bð Þ+ KF × SIMF A, Bð Þ,

SIMR A, Bð Þ = KT × SIMT A, Bð Þ + KD × SIMD A, Bð Þ+ Kd × SIMd A, Bð Þ: ð6Þ

Among them, KS is the weight of similar target size;SIMSðA, BÞ is the similarity of target size in two scenes; Kθis the weight of similar target direction [20].

From engineering considerations, digital image process-ing generally includes three levels: image processing, imageanalysis, and image understanding. Image processing is to“process” the image itself to improve its visual effect or formof expression and lay the foundation for image analysis. Theoutput of image processing is still an image with a new face.Image enhancement, image smoothing, image sharpening,image restoration, image transformation, image coding,image calculation (arithmetic or logical operations onpixels), etc. fall into this category. Image analysis is to detectthe target image in the frame and calculate various physicalquantities to obtain an objective description of the image.The main content includes image segmentation, imagefeature (geometric shape feature, boundary description,

and texture feature), and extraction. The output of imageanalysis is a set of data describing the characteristics andproperties of the target image. Image understanding is basedon image analysis, understanding the content represented bythe image, analyzing the relationship between the images,and obtaining an explanation of the objective scene and thenusing knowledge and experience, using mathematical, phys-ical, virtual, and reasoning methods. Harmony means estab-lish a rigorous or vague deductive mode and associate, think,and infer by the computer, so as to realize the description,understanding and service of the objective world. Imageunderstanding is closely related to pattern recognition, arti-ficial intelligence, and expert systems.

The mathematical expression of gray linear transforma-tion is

g i, jð Þ = gb − gaf b − f a

× f i, jð Þ − f a½ � + ga: ð7Þ

In the formula, f a and f b are the low and high grayvalues of the linear transformation section of the inputimage; ga and gb are the low and high gray values of the lin-ear transformation section of the output image, respectively.

Since the digital image is in discrete form, differentialoperation is used instead of differential operation, which isexpressed as

G f x, yð Þ½ � = f x, yð Þ − f x + 1, yð Þ½ �2 + f x, yð Þ − f x, y + 1ð Þ½ �2� 1/2:

ð8Þ

After the hybrid feature construction is completed, themethod based on key frames can be used to perform thehybrid feature matching and calculate the camera motion-related parameters. Specifically, the degree of dispersion ofeach stable feature line is determined by the number of opti-mized feature points, the number of stable feature lines, andthe length of the feature line:

N temp =Nt

λpNp + λ1N1×

N1 × Li∑N1

m=1Lm×Nmax

" #,

Ni =

2, N temp < 2,

N temp, 2 ≤N temp ≤Nmax,

Nmax, N temp >Nmax:

8>><>>:

ð9Þ

Among them, Ni is the number of points after the ithstable characteristic line is discrete.

When performing image forward matching, assumingthat the search window of the previous frame image featureIk−1ðxk−1, yk−1Þ is wk−1ðxk−1, yk−1Þ, the expression for calcu-lating the sum of squares of the feature pixel differences ofthe two frames using Tk−1ðu, vÞ as the matching templateis as follows.

3Journal of Sensors

Sk u, vð Þ =〠u,vTk−1 u, vð Þ Ik xk + u, yk + vð Þ − Ik−1 xk−1 + u, yk−1 + vð Þ½ �2:

ð10Þ

After traversing the image feature in the current frameimage matching search window, the smallest Skðu, vÞ isobtained. When the value is less than a certain threshold,the image feature corresponding to the value is used as thecandidate matching feature. At the same time, calculate thesum of squared pixel differences Sk−1ðu, vÞ with Tkðu, vÞ asthe matching template:

Sk−1 u, vð Þ =〠u,vTk u, vð Þ Ik−1′ xk−1′ + u, yk−1′ + v

� �− Ik xk + u, yk + vð Þ

h i2:

ð11Þ

The actual structure of the computer network is shownin Figure 1. Computer network technology is a combinationof computer processing technology and communicationtechnology. Therefore, from a traditional point of view, thebasic structure of a computer network can be divided intotwo parts: data processing and data communication accord-ing to logical functions; that is, it can be divided into twoparts: resource subnet and communication subnet. Thecommunication subnet provides network communicationfunctions and completes communication tasks such as datatransmission, exchange, control, and transformation amonghosts on the entire network, responsible for the data trans-mission, forwarding and communication processing of theentire network.

2.2. English Teaching Mode. Unremittingly carrying out theevaluation of English teaching quality in higher vocationalcolleges, on the one hand, can ensure that the leadership ofthe college and the teaching management department fullyunderstand the English teaching situation and provide newideas for the school’s English reform and innovative devel-opment. On the other hand, the positive interaction betweenteachers and students relies on a good teaching atmosphere.Teachers’ continuous innovation of teaching content, teach-ing methods, teaching attitudes, and active cooperation withstudents are the prerequisites for the successful completionof the teaching process, and teachers continue to improvein practice. Teaching ability is a necessary guarantee for stu-dents to improve their English level in practice. Teachingand learning complement each other and promote eachother to achieve the common progress of teachers and stu-dents [21, 22].

Constructive English teaching mode requires highervocational teachers to change traditional teaching conceptsand grasp the goals of higher vocational English teaching.In English dialogue and communication, teachers communi-cate and interact with students as equals. They do notimpose their opinions, thoughts, and feelings on students,but allow students to truly express their opinions, thoughts,and feelings, so as to obtain progress and constructionknowledge structure [23, 24]. For example, in oral Englishteaching activities, teachers can design corresponding ques-

tions around the theme and provide some relevant knowl-edge information to minimize the correction of languageerrors so that students can express fluently and freely. Onlyin this way can English teachers understand the students’real ideas, organize classroom activities smoothly, and con-trol the learning progress [25, 26]. Computer-aided Englishteaching mode emphasizes that students are the main bodyof the learning process and the active builder of knowledge,so it is conducive to students’ active exploration, active dis-covery, and the cultivation of students’ foreign languageapplication ability. However, this kind of teaching modeoveremphasizes students’ learning and often ignores the roleof teachers, the emotional communication between teachersand students, and the role of emotional factors in the learn-ing process.

The English teaching mode under the network environ-ment is shown in Figure 2. In the part of personalizedcourseware generation, personalized courseware is automat-ically generated from students’ personalized database, learn-ing strategy database, knowledge object database, andeducational resources distributed on the Internet or gener-ated with the participation of teachers or indirectly gener-ated by students and stored in the personalized coursewaredatabase. This is a learner-centered learning environment.When a learner enters the learning system, the personalizedinformation collection module and the evaluation trackerfirst collect the students’ personalized information throughsome strategies and then form a new learning record accord-ing to the learners’ learning needs, the learners’ previouslearning situation (from the student database), and theirlearning process. Then, the course controller will presentthe relevant learning content to the learner from the person-alized courseware library according to the new learningrecords and add the new learning situation to the studentlibrary. The course controller will also add a class of repre-sentative personalized courseware formed in the process ofstudents’ learning to the personalized courseware library.

3. Improvement Experiment of EnglishTeaching Mode

3.1. Subjects. This experiment randomly selected 40 sopho-more students, including 10 boys and 30 girls. Divide theminto two groups evenly and record them as the experimentalgroup and the control group, with 20 students in each group.Their English proficiency is almost the same, and there is nosignificant difference.

3.2. Computer Network Teaching Environment. The databaseserver and page server under the three-tier architecture runon the same computer at the same time. During the develop-ment process, the client browser is used for testing. Thehardware environment of the system development environ-ment is as follows: (1) processor: Intel(R) Core (TM)2duoT7300@2.0GHz; (2) memory: 2.0G; and (3) hard disk:80G.

The software environment of the system developmentenvironment is as follows: (1) operating system: WindowsXP SP2; (2) development platform: MyEclipse6.5; (3)

4 Journal of Sensors

database: SQL Server 2000; (4) web server: apache-tomcat-6.0.35; and (5) other design tools that assist the system tocomplete: Rational Rose, Dreamweaver 8, Microsoft Visio2003, Photoshop CS4, etc.

3.3. Teaching Experiment. In the preexperiment test, a uni-fied English final test paper was selected, and the scores of40 students were entered into the computer. After that, theywill be taught in different ways in groups. The experimentalgroup chose the English teaching mode of computernetwork-assisted teaching. In the teaching process, the QQclass group commonly used by students was selected as theteaching platform for online communication and learning.QQ can be used in mobile phones and computer tablet lightdevices and can be used by teachers to transmit instructionalvideos, documents, pictures, PPT, etc. conveniently. Stu-dents can download videos and other files in the group forself-learning before class. Teachers and students can com-municate on course learning in the QQ group. The timeli-ness of QQ facilitates the communication and effectivelysolves the student’s self-learning platform before class. Thecontrol group still used traditional teaching methods tolearn. After the experiment, the students in the experimentalgroup and the control group were tested after writing intheir respective classrooms, and the teachers gave theirscores based on the college entrance examination scoringstandards. Use SPSS 17.0 software to analyze the students’scores, and compare the changes of the students’ pretestand posttest scores to get the experimental results.

3.4. Interview. Among the students surveyed, 5-10 studentswere randomly selected for interviews. Before and afterquestionnaire surveys and student interviews with teachersand students, interviews were conducted with 1-2 teachersin each school on the use of multimedia in English teaching.The main content of the interviews between students and

teachers is carried out around the open questions in thequestionnaire.

3.5. Teaching Evaluation. In order to achieve objective eval-uation as far as possible, teachers pay attention observingstudents’ state in each class and give objective evaluationthrough students’ submission of homework, test scores ofchapters, participation in group activities, and so on. There-fore, in this stage of teaching activities, teachers’ timely andcorresponding encouragement is very important for stu-dents, which will mobilize students’ learning initiative to acertain extent. The development of group cooperative learn-ing helps students learn to help each other and have win-wincooperation. In groups, tasks are done together, but it alsohelps students develop a correct sense of collective responsi-bility and fair play. Students can evaluate team membersfrom the aspects of activity enthusiasm and learning partic-ipation. Students’ self-assessment can be evaluated objec-tively by observing their preclass learning level, chaptertask completion, class attendance, group cooperation partic-ipation, and so on.

4. Results and Discussion

Table 1 shows the overall views of students on the learningresources of the computer network teaching platform. Fromthe average point of view, the average number of networkplatform resources is 2.88, and its average value is less than3, which means that students think that the number ofonline network resources is too small. This shows that stu-dents think that online learning content is difficult; the aver-age value of online platform resources for student learning is3.29, in which the average value is greater than 3, whichshows that students think that the use of network platformresources is better for learning.

Figure 3 shows the statistical results of the pretest andposttests of the degree to which different models are

Local areanetwork

Local areanetwork

Figure 1: The actual structure of a computer network.

5Journal of Sensors

beneficial to improving the ability of autonomous learning.Under the traditional teaching mode of teaching by teachers,25% of the students think it is helpful to improve the self-learning ability, and nearly half of the students feel average

about improving the self-learning ability. According to thequestionnaire data and interview, results show that offlineclassroom teacher-student interaction is good, and the effectof teacher-student interaction is good. At the same time, theinterview shows three problems in the classroom: teachershave a single way of asking questions; the requirements ofteachers’ tasks are not clear enough; and the offline classhours are less, and the connection degree with online auton-omous learning is not enough.

Table 2 shows the comparison results of pretest andposttest English learning interests between the control groupand the experimental group. It can be seen from the resultsof the pretest English learning interest that the results ofthe t-test can be seen, t = 0:457, p = 0:927 > α = 0:05, so itcan be considered that there is no significant differencebetween the two means, that is, the difference in the currentlearning interest of students in the control class and theexperimental class can be ignored. And the average value is

Personalized

information collection

Course controller

Evaluation tracking

Personalized coursewaregeneration

Import and exportinterface

End user

Teacher

Object editor

Monitor

LearnerStudentlibrary

personalizedcourseware

personalizationlibrary

Learningstrategy

Knowledgeobject

Externalinformation

Figure 2: English teaching mode under the network environment.

Table 1: Students’ overall views on the learning resources of thecomputer network teaching platform.

VariableMeanvalue

Standarddeviation

The number of network platformresources

2.88 0.684

The difficulty of network platformresources

3.26 0.616

The attraction of network platformresources

3.18 0.861

The effect of network platformresources

3.29 0.803

6 Journal of Sensors

small, so it can be considered that before the experiment, theexperimental class and the control class have basically thesame interest in English learning as a whole, and both showlow interest. The results of posttest English learning interestcan be seen, t = 2:573, p = 0:006 < α = 0:05, which can beconsidered that the two means have a significant difference.

The description of students’ online autonomous learningis shown in Figure 4. In terms of learning motivation, theaverage of students’ online learning initiative consciousness(3.04) and learning interest (3.05) is slightly higher than 3,which indicates that students’ English learning motivation

is general; in terms of learning methods, the average valueof students choosing to solve problems independently(3.69) is greater than 3.5, while the average value of choosingto seek help from others is 2.72. This shows that in the pro-cess of online autonomous learning, most students choose tosolve problems independently and seldom seek help fromothers; in terms of learning content, the average value of goalsetting (3.46) is greater than 3, and the content absorption(3.15) is greater than 3, which indicates that many studentshave clear learning objectives and requirements in onlineautonomous learning and can fully absorb online learning

4

6

19

10

1

11

15

9

4

1

Very advantageous

More favorable

Feel average

Unfavorable

Very unfavorable

Score

Diff

eren

t situ

atio

ns

Post-testPre-test

Figure 3: Pre- and posttest statistical results of the degree to which different models are beneficial to improving the ability of independentlearning.

Table 2: Comparison results of pretest and posttest English learning interests between the control group and the experimental group.

Test Class Average Standard deviation t value Degree of freedom p value Mean difference

PretestControl class 1.13 1.78

0.457 83 0.927 0.454Experimental class 1.76 1.57

PosttestControl class 2.65 0.45

2.573 80.016 0.006 -3.658Experimental class 5.48 0.43

0

0.2

0.4

0.6

0.8

1

1.2

0 1 2 3 4

Active consciousness

Learning interest

Solve independently

Others help

Goal setting

Content absorption

Time plan

Self-monitoring

Self-summary

Self-evaluation

Stan

dard

dev

iatio

n

Average value

Var

iabl

e

Average valueStandard deviation

Figure 4: Description of students’ online autonomous learning.

7Journal of Sensors

content; in the management of learning process, the mean ofstudent time planning (3.29) is greater than 3, and the self-monitoring average is 3.41 which is greater than 3, whichshows that many students can self-monitor and reasonablyplan online self-learning time; however, the situation ofself-examination is not optimistic, and the mean of self-evaluation (2.71) and self-evaluation (2.94) is less than 3,which indicates that most students do not self-reflect andevaluate online self-study.

Figure 5 shows the comparison of the results of the testsubjects before and after the test. It can be seen from the fig-ure that after a period of study, the test subjects’ Englishscores have improved. However, the English scores of theexperimental class have improved more significantly. Withthe emergence of the gap between the English performanceof the experimental class and the control class, let us clearlyrealize that computer-assisted teaching is more conducive toimproving student performance. The gap between the over-

all average score and the excellent rate is constantly increas-ing, which means that the effect of English differentialteaching under the guidance of computer network-assistedteaching on English learning performance is positive.

The test results of the two classes were analyzed by SPSSstatistical software, and the results are shown in Table 3. Theaverage score of the experimental class was 94.22, and theaverage score of the control class was 89.97; the highest scoreof the two classes was 100, but the lowest score of the exper-imental class was 84, which was higher than the lowest scoreof the control class of 80. Therefore, compared with the tra-ditional teaching mode, the flipped classroom teachingmode based on micro lesson has better effect on promotingstudents’ performance.

The distribution of scientific research focus of Englishteachers in different industries is shown in Figure 6. Profes-sional English teachers who are transformed from publicEnglish teachers will still focus on language points in their

65.00

70.00

75.00

80.00

85.00

90.00

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39

Scor

e

Number

Pre-testPost-test

Figure 5: Comparison of the results of the test subjects before and after the test.

Table 3: Test result statistics of experimental class and control class.

Class N Mean Max Minimum Standard deviation Standard error of the mean

Control class 36 89.97 100 80 5.833 0.972

Experimental class 36 94.22 100 84 4.811 0.802

0 20 40 60 80

Linguistic theory

Classroom teaching reform

Curriculum construction

Practical reform

Value

Rese

arch

focu

s

Frontline teachers from enterprisesPart-time teacher of professional coursesPublic English transition teacher

Figure 6: Distribution of scientific research focus of English teachers in different source industries.

8 Journal of Sensors

teaching. For scientific research, they mainly study linguistictheories and classroom teaching reforms; professionalteachers are part-time teachers who teach industry English.In the process, more emphasis is placed on the explanationof professional knowledge and not much attention to lan-guage points, which has led to the problem of “focusing onprofessionalism and neglecting language” in many class-rooms. The scientific research of these teachers is mainlyabout classroom teaching reform and curriculum construc-tion; from industry English teachers on the front line ofenterprises can integrate theory with practice in teachingand ingeniously integrate language knowledge and profes-sional knowledge to teach students vividly. This part ofteachers is the most popular among students.

5. Conclusions

After the integration of computer networks and foreign lan-guage education, the educational model has changed, andthe traditional teacher-centered model has been broken.Foreign language education not only emphasizes thestudent-centered education model but also emphasizes theuse of the latest information technology to promote stu-dents’ independent learning of information. Taking intoaccount the rapid changes in computer technology in thefield of computer-assisted English education, teachingmethods continue to introduce new knowledge, and thebasic purpose of training teachers is to cultivate the abilityof English teachers to learn for life. Because the content ofthe training is briefly explained, the teacher who receivesthe training can remember the content of the training. Inorder to enable trained teachers to continuously improvethe level of lifelong education, apply the knowledge learnedby trained teachers to encourage their own learning. Afterthe integration of computer network and foreign languageteaching, the structure of teaching materials has changedinto three-dimensional and multimedia teaching materials.However, from the current situation, the three-dimensionalform of teaching materials is reflected in the physical con-cept of the composition of the teaching materials, especiallythe network teaching content which has become a copy ofthe paper teaching materials, and the effective compositionof the teaching materials should be that the network contentis an extension of the paper textbook rather than a copy.

Data Availability

No data were used to support this study.

Conflicts of Interest

The author declares that there are no conflicts of interestregarding the publication of this article.

Acknowledgments

This work was supported by the Planning Projects for “13thFive-Year Plan” of Shaanxi Education Sciences PlanningSGH20Y1170.

References

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