# Evaluation System of English Blended Learning Effect Based on Big Data Technology

Authors: Ling Yang
Journal: Security and Communication Networks
 (2022)

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## Abstract

In order to improve the effect of English teaching, this paper combines data mining algorithms to construct an intelligent learning system. Moreover, starting from the actual situation, this paper combines the actual needs of English teaching to construct a blended learning system framework. Blended learning provides a new idea for English teaching. As a result, the purpose of this work is to do some empirical research on how to successfully implement blended learning in college English classes. The findings of the study suggest that blended learning may help students improve their English application skills. Furthermore, this article discovered that students’ independent learning capacity, learning passion, and systematic comprehension of maritime equipment were enhanced to some amount using data mining and cloud classroom blended learning resource construction and teaching practise. Finally, this work integrated theoretical and experimental research to demonstrate that the system developed in this paper has a specific impact.


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## Body

## 1. Introduction

The twenty-first century is an era of information. The rapid development of information technology has profoundly affected all aspects of social life, and at the same time brought educational changes. Blended learning combines the advantages of traditional learning methods with the advantages of E-learning (i.e., digital or networked learning) [1], and has gradually received widespread attention from the English education community. However, some studies have found that the educational concept of blended learning is less applied in English teaching in higher vocational colleges, and empirical research is lacking. Therefore, how to use empirical research to explore effective ways to carry out mixed English learning in higher vocational colleges based on the purpose of English teaching in higher vocational colleges and the actual situation of higher vocational colleges is an important topic [2].The class teaching method offers several benefits in terms of cost-effectively fostering talents on a broad scale, providing full play to instructors’ leadership roles, giving full play to students’ collective roles, and supporting students’ multi-faceted development. This is also one of the reasons why, after centuries of evolution, this teaching organisation style is still one of the primary teaching methods in the fields of basic education and higher education [3].With the rapid development of Internet education and the continuous improvement of educational goals and requirements, traditional face-to-face classroom teaching has gradually revealed its own flaws and defects in the wave of informationization, which are primarily reflected in the four aspects below. First, teachers mostly use instillation teaching methods, and students passively accept ready-made knowledge, which is not conducive to cultivating practical and innovative abilities; second, the teaching process emphasises concentration and unity, and it is difficult to implement hierarchical teaching, taking into account individual differences among students, and achieve success; and third, the teaching process emphasises concentration and unity, and it is difficult to implement hierarchical teaching, taking into account individual differences among students, and achieve success. Teach students according to their aptitude; third, teaching organisation activities are singular and lack flexibility, which is not conducive to the development of advanced thinking; fourth, teaching evaluation frequently focuses on results while ignoring the learning process, which is not conducive to cultivating learning enthusiasm.As a consequence of the penetration of information technology in the field of education technology, a variety of digital learning approaches have emerged. Information-based learning resources have become more numerous and faultless as digital learning methodologies have advanced. There are tablet PCs, PDAs, and smart phones to choose from. Notebook computers, mobile learning devices, and their network connections have greatly enlarged the learning options available to today’s college students, and online learning is growing more popular. Online learning is gradually replacing traditional learning and teaching methods as the shortcomings and inconsistencies of traditional teaching strategies become more evident. After a long period of experience and evaluation, people have gradually realised that online learning has insurmountable drawbacks. The aggregation and reflection of people’s learning experiences throughout time gave rise to blended learning. Blended learning shines not only in early corporate training, but also in school education, particularly in the area of college education, as the theory and practise of blended learning improves, as does the availability of online resources ideal for blended learning. It is a priceless educational tool.This article combines big data technology to evaluate the effect of English blended learning and builds an intelligent evaluation system to improve the quality of English blended learning.


## 2. Related Work

Foreign research on blended learning begins with a reflection on the defects of online learning and traditional classroom teaching. In terms of component definition, the literature [4] regarded blended learning as an organic composition of three parts: learning in a supervised physical location, an integrated learning experience, and online learning. It is emphasized that blended learning is a formal education project, so some special cases are excluded, such as the situation where students play educational games at home or browse learning software in online stores outside of the school’s formal teaching courses. In terms of application model construction, the literature [5] divided the blended learning model into skill-driven model, attitude-driven model and ability-driven model. The literature [6] proposed four models of blended learning, namely: web-based transmission, face-to-face processing, forming a certain product, and collaborative extended learning. The mixed learning mode was classified as conversion mode, flexible mode, menu mode, and improved virtual mode in the literature [7]. The conversion mode is the most common among them. Students must switch into any course or topic according to a set schedule or the teacher’s arrangement under this sort of scheme. At least one of these learning modules is based on online learning. In terms of system and instructional design, the literature [7] highlighted five important aspects of blended learning system and instructional design based on current research on instructional design. They are collaborative learning, which helps students improve their learning and inquiry abilities in teaching activities, classroom teaching activities in which teachers participate, learners’ independent learning based on their personal abilities, diversified evaluation methods that focus on the process and results, and various learning supports that help students improve their learning effects. There are other studies on the content form, validity verification, development trend, and blended learning application in businesses [8]. A long-term follow-up research on the deployment of blended learning in corporate training was undertaken in the literature [9]. After evaluating and summarising hundreds of examples, it has been shown that employee job performance may be enhanced via blended learning, resulting in better enterprise production efficiency and increased corporate output value. IBM developed the Basic Blue for Manager course based on the blended learning theory to obtain improved management design as a result of this effect. In terms of MOOC application, several new advancements in blended learning application research have developed after the advent of MOOC. Professor Kathy Davidson of Duke University launched the Coursera platform with a MOOC titled “History and Future of Higher Education” [10], and the literature [11] used MOOC to blended learning and developed SPOC (Small Private Online Course). This innovative style of instruction may improve not just the number of teaching methods available to instructors, but also student throughput, knowledge mastery, and participation. The literature [12] used blended learning to make up for the learner’s shortcomings due to limited laboratory use time. From the above analysis of the current situation, it can be found that foreign research in the field of blended learning has achieved good results, and the research results are also relatively fruitful. It mainly includes related research on the definition of mixed components, construction of application models, system and instructional design, and application in enterprises [13]. In the sweep of the digital tsunami, the research of blended learning has also been combined with MOOC [14]. As time goes by, the theoretical research on blended learning will become more stable, more diversified, and the field of applied practice will become broader [15]. Therefore, we should pay attention to the international development trend of blended learning, review the current situation with a wise perspective, actively absorb successful experience from abroad, enrich our current research, and provide some reference value for teaching reform.


## 3. Application of Big Data Processing Technology in the Evaluation of English Blended Learning Effect

The goal of variable selection is to choose a subset of variables from a vast number of original variables that is representative or has a significant influence on the dependent variable. Many variables are picked in practical applications that have no meaningful influence on the dependent variable and will impair the interpretation of the findings as well as the accuracy of the estimate. The dimensionality of the data analysed in this study is pretty large, and the qualities between the variables are relatively near, which is one of the primary features of the data. As a result, before evaluating the data, it is first preprocessed to look at the link between the independent variables. The link between the rele-vance and the rele-vance. If there is a substantial correlation between the two independent variables, it means there is collinearity between them, and the two independent variables have the same or comparable impacts on the dependent variable, thus one of them should be deleted.If there is a strong link between the qualities, it means they are similar or identical to the information from the data analysis, and one of them may be removed. As a consequence, prior to the initial screening of unique variables, this research uses the Pearson correlation coefficient approach to calculate the correlation coefficient between the two independent variables [16].The Pearson correlation coefficient reveals how strong the two independent variables are linked. Assuming there are two variables A and B andn samples, the Pearson correlation coefficient calculation procedure for A and B is as follows: [17]:(1)rAB=∑i=1nAi−A¯Bi−B¯∑i=1nAi−A¯2∑i=1nBi−B¯2n!r!n−r!.In the formula,Ai and Bi respectively represent the observed values of variable A and variable B in the i-th sample, and A¯ and Ai represent the average of all observed values of attribute A and attribute B, respectively. It is calculated that the value interval of Pearson’s correlation coefficient B¯ is [-1, 1]. When there is −1≤rAB<0, A and B are negatively correlated; when there is 0<rAB≤1, it is positively correlated; when there is rAB=0, it means irrelevant. The closer the absolute value of Pearson’s correlation coefficient is to 1, the stronger the correlation between the two variables. For a sample containing p initial variables, a p×p order Pearson correlation coefficient matrix R can be generated. Any element in the matrix represents the Pearson correlation coefficient of the two variables corresponding to its row and column. It is easy to know that the diagonal elements of the matrix are all 1.In this paper, ifrAB≥0.8 is set, it means that there is a strong correlation between the two variables, which means that the two independent variables have the same or similar effects on the dependent variable, so one of them should be eliminated [18].We assume that the model is:(2)Y=mX+ε.Among them,X=X1,…,XpT represents 1 to p independent variables, m(X) will be discussed below. Whether in theory or in practice, the relationship between the independent variable and the dependent variable is not a simple linear relationship (the empirical explanation below). Therefore, the above model should not be a simple linear regression model, then there is(3)mX=∑j=1pfjXj.Then, the nonparametric regression ofp variables separately can be obtained as follows:(4)minfj∈L2PEY−fjXj2.The above formula is minimized to [19](5)fj=EY|Xj.In order to better reflect the nonlinear influence of the independent variable on the dependent variable, here we use the nonparametric regression estimation based on the B-spline, that is, perform the B-spline base expansion on each independent variable separately, and the form is as follows:(6)fnj=∑k=1dnβjkψjkx.Among them, there is1≤j≤p.In order to estimatefj, we use the nonparametric form based on B-spline to expand, and use the sample mean to estimate fj to get(7)minfnj∈SnEY−fnjXj2=minβj∈RdnEY−ψjTβj2.Among them, there isψj=ψjXj=ψjXj,…,ψdnXjT.Then, by sorting the obtained estimates, the variables that have a strong correlation with Y are filtered out [20]:(8)M^vn=1≤j≤p:f¯njn2≥vn.Among them, there isf¯njn2=n−1∑i=1nf^njXij2.In other words: some variables with relatively small residuals obtained by nonparametric regression estimation of each variable are selected, so that(9)μj=minβjEY−ψjTβj2.It satisfies(10)N^γn=1≤j≤p:μj≤γn.Data screening process:The first step is to determine the B-spline basis’s expansion form (ie the choice of degrees of freedom).Because the English blended learning assessment system is a nonlinear system, the traditional linear regression model may miss important information, and the results obtained are unable to adequately explain the dependent variables, so this article employs the cubic B-spline (Basis-Spline) expansion. The following formula uses the specified form as a starting point. The cubic B-spline, as can be seen, comprises the first, quadratic, and cubic terms of the independent variable, followed by the piecewise function. The piecewise function’s node changes for various samples. This approach is referred to as nonparametric regression in this article since it is not fixed.The cubic B-spline regression function is a piecewise function that may be taken at a given ratio in range space, except in rare cases. The degree of freedom, or the number of words in the f(x) equation, is the smallest value ofn.The degree of freedom of the expansion should be defined before to regression analysis. The accuracy of the regression result is affected by the degree of freedom value. This research used the approach of many fold cross-validation (cV) to determine the degree of freedom corresponding to the best regression effect. The procedure of calculating the optimum degree of freedom of the regression function for a given variable is as follows:(1)
Set the value interval of the degrees of freedom [3,n], and initialize the degree of freedom label j = 3.(2)
Divide all samples intom groups evenly in order, and initialize i = 1.(3)
Take the degree of freedom asj, and use the rest of the samples outside the i-th group as the training set to perform nonparametric regression. (4) Using the samples in the i-th group as the test set, calculate the RSS of the regression model.(5)
Ifi≠m, then i = i+1, return to step (3); if i = m, find the mean value of m RSS under the condition of degree of freedom j, as the RSS of degree of freedom j.(6)
Ifj≠n, then j = j+1, return to step (2); if j = n, all the algorithms are over, and the RSS corresponding to each degree of freedom is extracted.(7)
Sort the RSS, and the one corresponding to the smallest RSS is the optimal degree of freedom of the attribute regression function. Step 2: perform preliminary variable screening.In the determination of the B-spline base expansion form in the first phase, where each independent variable is the optimal cubic B-spline (Basis-Spline) expansion, each independent variable must be fitted to the dependent variable. Sort the respective residual sums of squares according to the quality of the fit, and then choose the initial portion of the independent variables that are reasonably well-fitted for the subsequent variable screening and fitting. Some of the independent variables chosen here have a dramatic decline in the sum of squares of the residuals of each variable. The chosen independent variables and expansion items are joined to generate a new independent variable after the preliminary variable screening.First of all, the independent variables and dependent variables are in the form of nonparametric additive models. The specific model forms are as follows [21]:(11)Y=μ+∑j=1pfiXj+ε.Among them,p is the number of variables, that is, the number of variables in the preliminary screening above. Each variable still needs three Basis-Spline to expand (the optimal expansion item of each variable has been determined in the nonparametric independent variable screening and can be used directly), that is, B-spline is performed on fnjx Base expansion, the form is as follows:(12)fnjx=∑k=1mnβjkϕkx.Among them, there ismn=df∗p and df is the degree of freedom, that is, the number of independent variables in various forms. Penalty least squares are considered:(13)Lnβn;λ=∑j=1nYi−∑j=1p∑k=1mnβjkϕkXij2+λn∑j=1pωnjβnj2.The identifiable conditions are met:∑j=1p∑k=1mnβjkϕkXij=0.The above identifiable condition is to centralize the processing of data:(14)ψjkx=ϕkx−ϕ¯jk.We makeZij=ψj1xij,ψj2xij,…,ψjdxijT.Then, the design matrix can be expressed as:Z=Z1,Z2,…,Zp.Second, since each variable has been increased by three Basis-Spline after processing, it may be considered a group of groups. As a result, the Group-Lasso estimate approach is considered in this study. This approach has little data requirements, and variable filtering may be done at the same time as estimate in the form of group filtering (that is, the condition for this variable to be selected is that the coefficients of all expansion items of the independent variable cannot be Is zero).Theωnj=1 in the above formula can be transformed into the following unconstrained based on the constrained penalty least squares in the third step of the centralization result. Therefore, the objective function is(15)Lnβn;λ=Y−Zβn22+λn∑j=1pβnj2.The above formula is minimized to(16)β˜n=β˜nλn1argminβnLnβn;λn1.The Pearson correlation matrix and regression function based on cubic B-spline expansion were used to screen the independent variables in the aforementioned data preparation and nonparametric independent variable screening. It develops the first feature attribute set by removing redundant independent variables as well as independent variables that are not strongly connected to the dependent variable. However, the nonparametric independent variables (NIS) method can only offer a one-to-one description of the correlation between each independent variable and the dependent variable, and it cannot account for the system’s nonlinearity and interconnectedness. As a result, all of the identified independent variables must be combined for further screening and estimate.


### 3.1. Data Centralization Processing

According to the introduction to the model establishment in the previous section, the data should be centralized here. In this case, the following regression equation does not have an intercept term, and the central processing formula is as follows:(17)ψjkx=ϕkx−ϕ¯jk.


### 3.2. The Determination of the Penalty Coefficient in the Group-Lasso Algorithm

After the above-mentioned centralization of the data, before the estimation, the optimal selection of the penalty coefficient in the Group-Lasso algorithm is required. The selected method is the multi-fold cross-validation (CV) method. The specific selection process is as follows:(1)
The algorithm sets the penalty coefficient input range as [0.1, 1], and the initialized penalty coefficient input isj.(2)
The algorithm divides all samples intom groups evenly in order, and initializes i = 1.(3)
The algorithm takes the penalty coefficientλ as 0.1, takes the rest of the samples outside the i-th group as the training set, and uses the Group-Lasso algorithm to estimate the nonparametric additive model.(4)
The algorithm uses the samples in the i-th group as the test set to calculate the RSS of the regression model.(5)
If there isi≠m, then there is i = i+1, and the algorithm returns to step (3); if there is i = m, the algorithm obtains the average value of m RSS under the condition of j as the penalty coefficient λ.(6)
If there isj≠n, then there is j = j+0.1, and the algorithm returns to step (2); if there is j = 1, all the algorithms are over, and the RSS corresponding to each degree of freedom is extracted.(7)
The algorithm sorts the RSS, and the penalty coefficient corresponding to the smallest RSS is the optimal penalty coefficient.(8)
The algorithm uses the Group-Lasso algorithm to estimate the nonparametric additive model.


## 3.1. Data Centralization Processing

According to the introduction to the model establishment in the previous section, the data should be centralized here. In this case, the following regression equation does not have an intercept term, and the central processing formula is as follows:(17)ψjkx=ϕkx−ϕ¯jk.


## 3.2. The Determination of the Penalty Coefficient in the Group-Lasso Algorithm

After the above-mentioned centralization of the data, before the estimation, the optimal selection of the penalty coefficient in the Group-Lasso algorithm is required. The selected method is the multi-fold cross-validation (CV) method. The specific selection process is as follows:(1)
The algorithm sets the penalty coefficient input range as [0.1, 1], and the initialized penalty coefficient input isj.(2)
The algorithm divides all samples intom groups evenly in order, and initializes i = 1.(3)
The algorithm takes the penalty coefficientλ as 0.1, takes the rest of the samples outside the i-th group as the training set, and uses the Group-Lasso algorithm to estimate the nonparametric additive model.(4)
The algorithm uses the samples in the i-th group as the test set to calculate the RSS of the regression model.(5)
If there isi≠m, then there is i = i+1, and the algorithm returns to step (3); if there is i = m, the algorithm obtains the average value of m RSS under the condition of j as the penalty coefficient λ.(6)
If there isj≠n, then there is j = j+0.1, and the algorithm returns to step (2); if there is j = 1, all the algorithms are over, and the RSS corresponding to each degree of freedom is extracted.(7)
The algorithm sorts the RSS, and the penalty coefficient corresponding to the smallest RSS is the optimal penalty coefficient.(8)
The algorithm uses the Group-Lasso algorithm to estimate the nonparametric additive model.


## 4. Evaluation System of English Blended Learning Effect Based on Big Data Technology

English instructors may swiftly promote the in-situ conversion paradigm and launch innovation. Bottom-up innovation may be supported by school administrators encouraging and aiding teachers to innovate, as well as establishing infrastructure for teachers to allow for growth of invention and performing professional skills training, such as Internet resources, software licencing, and so on. A plan for blended learning design is built based on these notions, as illustrated in Figure1.Figure 1
Blueprint for blended learning design.As indicated in Figure2, a four-stage model of blended learning design is developed. The examination of learners’ requirements is the first phase in Person’s model, and all following stages of growth are dependent on that initial step. This approach considers the variety of learners’ prior experiences, and thinks that the creation of blended learning should be student-centered, with the goal of maximising each participant’s learning efficiency.Figure 2
Four-stage model.On the basis of the four-stage model, it is refined and perfected into eight stages, as shown in Figure3. From the figure, it can be found that these eight stages are connected with each other, and the loops are tightly interlocked to form a loop body.Figure 3
Schematic diagram of the design steps of blended learning.As illustrated in Figure4, the “self-preparation and Q&A-classroom knowledge expansion” process based on the social platform is split into two parts: social platform self-preparation and Q&A, and offline classroom knowledge development and consolidation. Self-preparation is the initial level of learning. Preparatory requirements are issued by the instructor to enable pupils to preview the material by asking questions. The second step is to explain the fundamental information and improve the knowledge received by the pupils on this basis. Through replies to student questions and summary analysis, teachers filter out the key points that need to be reinforced in offline learning and further cement the information learned on social platforms and in classrooms. Learners get a greater grasp and mastery of information, complete learning activities, and meet learning objectives via offline learning.Figure 4
A flowchart of blended learning between offline learning and online Q&A.As shown in Figure5, the process of blended learning of “offline classroom learning—difficult review on social platform platform” based on social platforms is also divided into two parts. The first part is mainly classroom teaching. The basic knowledge of each text is explained clearly, and the homework assignment is done at the end. At the same time, at the conclusion of class, the instructor will explicitly clarify the review needs. The Q&A activity on the social network is the second component. Learners ask questions, instructors solve issues, and learners’ inquiries must align with the educational objectives. To adapt to the learning style of autonomously asking questions, blended learning needs learners to have a thorough understanding of themselves, high autonomous learning capacity, and a strong feeling of self-efficacy.Figure 5
A flowchart of blended learning between offline learning and online Q&A.The flowchart of the blended learning of offline learning and online group discussion is shown in Figure6.Figure 6
Flow chart of blended learning between offline learning and online group discussion.The procedure is separated into three sections. The offline classroom learning is the first component. This section is primarily for teachers to review and explain the knowledge points gained, from which they can better understand the students’ deficiencies or current problems; however, the most important thing is for students to understand and comprehend the content of the knowledge points they have learned, and then perform simple exercises. Outside of class, the second half consists mostly of group conversations on social media sites. The group discussion is not intended to address the challenges that all students in the class face, but rather to address the issues that those engaging in the discussion face in offline learning. As a result, the aims and issues of online and offline classroom conversations are distinct. This section’s material is heavily influenced by offline learning. Its major purpose is to provide exploratory topics so that students may learn to apply the information they have studied fully. After the student has completed the lesson, the instructor should provide an immediate assessment, which may be done using the instant evaluation platform for statistics or using evaluative language. The third section is mostly concerned with analysing the impact of pupils in order to prepare for the next modification plan.The teaching design of blended learning based on cloud classroom is divided into three links before class, during class and after class, as shown in Figure7.Figure 7
Teaching design of blended learning based on cloud classroom.Instructors provide learning materials before class, students study independently using an app, ask questions, and teachers respond interactively. Teachers plan teaching activities in preparation based on students’ preclass learning situations, and employ a variety of learning activities to increase student engagement, such as vocabulary collocation, group discussion, peer dialogue, personal reporting, and so on. Teachers should make full use of cloud classroom capabilities like screen projection, APP sign-in, shaking, random grouping, and real-time interaction to ignite students’ interest and enthusiasm for learning. Pupils should be actively encouraged to think and publish at the same time by their teachers. Developing critical thinking skills in students by allowing them to express their own opinions. After class, the instructor gives online assignments based on the teaching objectives and the degree to which the main and challenging issues were covered. Multiple choice questions, vocabulary translation, essay questions, mini films, PPT presentation, and other types of study assignments are used to detect and solidify students’ learning in the classroom above the line effect. Students’ independent learning is promoted before class, students’ inquiry and study are reinforced in class, and students’ deepening and consolidating learning is enhanced after class in blended learning teaching activities. A closed loop of online and offline benign complimentary is constructed before, during, and after class. To guarantee a seamless implementation of the teaching process and the achievement of teaching objectives, teachers must develop blended learning teaching activities throughout the process.


## 5. System Performance Analysis

After constructing an English blended learning effect evaluation system based on big data technology, the performance of the system is verified. Based on the actual situation, this paper combines simulation experiments to evaluate the data mining effect and teaching effect of the English blended learning system. The results are shown in Table1 and Figure 8.Table 1
System performance test.
NumberData miningTeaching effectNumberData miningTeaching effect192.0184.543194.3292.15297.0090.383297.2482.88394.2781.353397.6186.18493.7592.943494.2385.29597.2792.213594.2492.40697.6192.773695.8786.27796.3289.523797.1882.34896.5683.223894.2580.58996.3588.353992.8088.831093.3491.184096.9783.861194.6482.354192.3092.001296.7390.404296.8886.261395.8692.684394.7581.841494.0783.034493.1388.701597.4688.764593.7888.901697.7383.874695.2381.491796.2788.114796.5883.831894.8383.814896.1888.001996.1485.874992.1181.372093.9290.775096.9881.442197.9882.035195.1085.122296.1982.075294.4282.052393.2390.685393.6480.952495.1089.555493.6091.712594.9680.475592.7885.382692.7985.325696.8291.212797.2892.365793.1288.652897.9688.835893.9482.452992.7592.905993.7084.643093.7986.476095.4686.44Figure 8
Statistical diagram of the practical effect of the evaluation system of English blended learning effect based on big data technology.From the above research, it can be seen that the evaluation system of English blended learning effect based on big data technology constructed in this paper has good data mining effect and teaching effect.


## 6. Conclusion

Through data mining and cloud classroom blended learning resource construction and teaching practice, students’ independent learning ability, learning enthusiasm, and system understanding of marine equipment have been improved to a certain extent. Students accept and recognize cloud classroom blended learning resources and learning activities. However, in the “Engineering English” mixed classroom, it is discovered that there are few students online, there is inadequate online engagement, the learning initiative has to be strengthened, and the teaching materials need to be further enhanced and improved. All of these elements have an impact on the effectiveness of blended learning. Teachers must continue to enhance the development of cloud classroom blended learning tools and improve communication and interaction between online and offline teachers and students. Moreover, it is necessary to create an environment of autonomous learning and cultivate students’ learning initiative, strengthen the design and monitoring of online and offline learning activities, so that cloud classroom blended learning is more effective and more popular with students.



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