Design of English Teaching Resource Sharing and Remote Classroom Platform Based on Cloud Computing Technology

In this paper, we propose a network teaching resource management system based on network environment, which integrates the positioning information of various communication and transmission interfaces to generate a complete data collection and upload it to the digital teaching resource library [32]. Users can download the data to the cloud through the data transfer [33] function of cloud computing, and the data transfer function of the data can be expressed as follows: (1) a(s)=w2s2 + q

Where a is the time required to upload data from the cloud, w is the caching factor used for platform operation, s is the rate of data transfer, and q is the amount of information uploaded at one time. On this basis, it is assumed that each module of each cloud platform has the same data-aware environment, but different data types have their own characteristics. When performing resource attribute segmentation, set an attribute parameter associated with digital textbook materials. Then there are: (2) r2=(1 − e11)1+e11(k11 + e21)2

In the formula, r represents the association between the attribute parameters and the cloud data; k11 represents the cognitive coefficient of the characteristics of this data; e11,e21 represents the amount of data carried by the cloud at different moments. Constructing a specific relationship expression makes the degree of association between different attribute parameters and cloud data clearer, and realizes the establishment of digital English teaching resources database.

In constructing the integration model of quality teaching resources, this paper uses cloud computing technology as a parallel computing framework for information resources in the constructing model.

1)

Data Access and Mining When accessing these digital English teaching resources storage databases, Extensible Markup Language (XML) [34] is used to mine the information resources contained in the requests. As a universal data description language, XML can effectively describe and store various types of teaching resources, facilitating subsequent processing and analysis.

In this paper, the database for constructing a secure sharing platform for English teaching resources includes three parts: the teaching resources database, the user information database and the learning sign-in database. The English teaching resources database is mainly used to store information such as teaching resources of English courses. The user information database is mainly used to store data including the name, email address and personal profile of registered users, and administrators can manage the data after logging into the system for identity authentication. Different modularized teaching resources are constructed, and the data information is transformed into the form of database management platform records, so as to realize the internal sharing between information resources, and this mode is used as the interaction interface between the program and the database. The location information of each communication and transmission interface is summarized to form a complete set of data sets and uploaded to the platform cloud. The data is uploaded to the cloud by utilizing the data transfer function in the cloud. The expression of its information data transfer function is: (3) a(s)=w2s2+q

Where: a represents the length of time experienced by the data uploaded to the cloud; w represents the buffer coefficient of the platform operation; s represents the data transmission speed; q represents the amount of data uploaded on a single occasion. It is assumed that each module of cloud computing in the platform has the same data sensing conditions and different types of data have different properties. When segmenting the resource attributes, it is necessary to set an attribute parameter, and the expression of the relationship between this attribute parameter and the cloud data is: (4) r2=(1−e11)1+e11(k11+e21)2

Where: r denotes the relationship between the attribute parameters and the data in the cloud; k0 denotes the attribute-awareness coefficient of the data, and e11,e21 denotes the total amount of data that can be carried by the cloud at different moments.

The business process of the platform is the uploading and downloading process of teaching resources, and user identity authentication is set up in the login interface of the platform.

After successful login, the user enters the half-platform teaching resource sharing space, and completes the uploading and downloading of teaching resources in the space. Platform resource uploading is only for the platform administrator, ordinary student users are not authorized to upload teaching resources on the platform, so when users send upload resource service requests to the platform, the platform will check the user information. In order to distinguish between the two kinds of users, the platform divides the users into two kinds, S−**** and A−**** , in which the user beginning with S is a senior user, i.e., a user who has the authority to upload teaching resources, and the user beginning with A is an ordinary user, and the platform recognizes the user’s identity and examines whether the user has the authority to upload teaching resources. The identification process is expressed by the following formula: (5) f=∑N=1SNP

Where: f indicates the user identity authority level; N indicates the number of platform user identity authority levels; SN indicates the N th user identity authority level code; P indicates the first user identity information code, which is utilized to identify the user identity authority. In order to avoid duplication of teaching resources on the platform, duplication verification of uploaded teaching resources on the platform is designed, and the duplication rate of teaching resources is calculated by comparing the uploaded teaching resources with the resources in the cloud database with the following formula: (6) q=e/ω¯

Where: q denotes the duplication rate of teaching resources to be uploaded; e denotes the number of keywords in the uploaded teaching resources that are consistent with the keywords in the platform’s cloud database; ω¯ denotes the total number of keywords in the uploaded teaching resources.

The correlation coefficient between the keywords and the teaching resource files is calculated with the following formula: (7) k=∑n=1yn(m+a)/g

Where: k indicates the correlation coefficient between the keywords and the teaching resource files; n indicates the number of teaching resource files in the cloud data array of the platform; yn indicates the n th teaching resource file; m indicates the name of the teaching resource file and the semantic value; a indicates the semantic value of the content of the original file of the College of Teaching and Learning; and g indicates the semantic value of the keywords in the front. According to the size of the correlation coefficient value, the teaching resource files are sorted, and according to the order of arrangement, the original files of the teaching college are displayed on the platform display interface, and the lower right end of each file is designed as a down compare port, through which the user can down compare the teaching resource files on the surface, thus realizing the sharing of teaching resources.

The schematic diagram of the architecture of the open sharing platform for teaching resources is shown in Fig. 1. The platform is structured on a cloud computing platform, with (1) a service layer, which is responsible for providing users with a direct entrance to the sharing service, and users can access the sharing platform at any time through computers and mobile devices. (2) Management layer, responsible for providing the level of platform management functions, the cloud computing provider manages and maintains hardware devices and software storage and computing and other functions. (3) Resource layer, the key part of realizing the function of integrating teaching resources, where each school uploads its own teaching resources to the platform’s cloud resource pool to realize virtual storage through a unified interface.

Figure 1.

Architecture diagram of the open sharing platform of teaching resources

The main task of resource integration is to transfer the high-quality English teaching resources stored in the original platform to the cloud platform and to complete the resource information according to the resource structure of the cloud platform. Since the traditional distance learning resource sharing platform adopts certain information maintenance means during the construction, it is not difficult to synchronize the existing English teaching resources to the cloud platform, and the completion of the complementary information on the cloud platform can be continued to display the teaching on the cloud platform.

The educational application service design part mainly includes 4 parts, namely, educational storage service, remote classroom service, faculty management service and educational portal service.

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Educational Storage Service. The educational storage service constructed by the remote teaching resources sharing platform centralizes the storage of high-quality English teaching resources in the Hadoop storage cluster, so that English teaching resources can be read and used quickly and conveniently.

The distance learning resource sharing platform [35-36] specifically includes two parts: physical resources and resource pool. This layer mainly uses the virtual technology of cloud computing to centrally manage the platform hardware equipment, establish a virtualized hardware resource pool, and provide shared services such as storage, computing, and data resources. Physical resources are composed of computer network infrastructure equipment, servers, databases, etc. of major universities, key educational institutions, and training institutions.

When the entire distance learning resource sharing platform module starts to run, first of all, you need to click on the ID value of the resource contained in the resources field value in the resource review page, and at the same time, utilize the get-all within the request to open the platform storage system, from which you need to find the teaching resources for review. After that, the get-by-id method within Resource is utilized to get the basic information related to the teaching resource. Finally, the old and new information is integrated and centrally reviewed to obtain the review results, which are kept by the storage terminal.

In this paper, firstly, cloud computing technology is adopted to realize data sharing, and secondly, Byzantine fault-tolerant consistency algorithm is utilized to construct the identity authentication system of digital teaching resources under the premise of ensuring data security. In this architecture, virtual data are treated like ordinary nodes, while other nodes may contain invalid data or suffer from malicious attacks, and the total number of nodes in the framework |R| Eq: (8) |R|=3f+m

Where, f is the virtualized data node and m is the number of invalid nodes.

The master node of the authentication architecture performs hash value checking instantly when a user makes a request to share data with other users.

The program is denoted as: (9) M=Hi(K−G)n

Where M is the data that has passed the hash value verification, Hi is the hash value of data i , K is the source area of the data sharing request, and G is the number of nodes traversed by the data verification. Through the hash value test, to determine whether the data on the request side is real, valid, so as to ensure the security and reliability of the data.

The survey statistical results of the experimental pre-test of learning interests of the experimental and control classes are shown in Table 3. The results show that in the results of the experimental pre-test, the results of the learning interest survey of the students in the two classes on the three questions I like to take English classes, I will feel happy in English classes, and I will take the initiative to communicate in English are very close to each other, and there is almost no difference.

The results of the students’ learning initiative survey are shown in Table 4. The results show that there is very little difference in the students’ satisfaction with the results of the survey on learning initiative with regard to the three questions of I am willing to actively participate in all the activities in English class, I am willing to spend more time on English learning after class, and I can take the initiative to complete my English homework. Specifically, the percentages of students in the control class who chose to conform to the descriptions of the above three questions were 32%, 32% and 38% respectively; the corresponding percentages of students in the experimental group chose to conform to the descriptions were 31%, 34% and 37% respectively. And the percentage of students in both classes for the other options is similar to that of the conforming option. Obviously, there is no significant difference in the results of the survey on learning initiative between the students of the two classes before the beginning of the experiment.

The results of the students’ learning efficiency survey are shown in Table 5. In the results of the students’ survey on learning efficiency, there are very small differences in their satisfaction with the three questions: my concentration in English class, compared with the previous semester, I think my English learning efficiency has improved, and compared with the previous semester, I have made progress in listening, speaking, reading, writing, and so on. Specifically, the percentage of students in the control class who chose to conform to the descriptions of the above three questions was 32%, 8% and 11% respectively; the corresponding percentage of students in the experimental group chose to conform to the descriptions of the above three questions was 31%, 7% and 10% respectively. And the percentage of students in both classes for the other options is similar to that of the conforming option. Obviously, there is no significant difference between the results of the two classes of students on the study efficiency survey before the beginning of the experiment.

Based on the above statistical results, this paper uses SPSS software to conduct independent samples T-test on the questionnaire survey pre-test data. The statistical results of the independent sample t-test group of the questionnaire pre-test are shown in Table 6. The mean values of the students’ responses to the nine questions in the control group ranged from 1.8611 to 2.7888, and those in the experimental group ranged from 1.8768 to 2.7819. It can be seen that the difference between the questionnaire results of the control class and the experimental class is not obvious, and the subsequent experiment can be carried out normally.

At the end of the experiment on resource sharing remote classroom teaching with the assistance of cloud computing technology for the experimental class, this paper conducts a post-test of the questionnaire for the experimental class and the control class, and the content of the post-test form is exactly the same as the content of the pre-test. The results of the experimental post-test survey on the learning interests of the students in the two classes are shown in Table 7. The results show that the percentage of students in the control class who chose the compliant options for the three questions I like to take English classes, I will have fun in English classes, and I will take the initiative to communicate in English were 43%, 37%, and 19%, respectively; and in the experimental class it was 65%, 62%, and 37%. The percentage of students in the experimental class who chose the non-compliant option for the three questions in the results of the survey on learning interest decreased by 8%, 25% and 15% respectively compared to the control class. It can be seen that after adopting the teaching method proposed in this paper, the experimental class students’ interest in learning English improved significantly compared with the control class students.

The results of the post-experimental survey on students’ learning initiative in both classes are shown in Table 8. The results show that students in the two classes varied greatly in their satisfaction with the three questions in the learning initiative survey results, namely, I am willing to actively participate in all activities in English class, I am willing to spend more time on English learning after class, and I can take the initiative to complete my English homework. Specifically, the percentages of students in the control class who chose to conform to the descriptions of the above three questions were 30%, 35% and 38%, respectively; the corresponding percentages of students in the experimental group chose to conform to the descriptions were 62%, 52% and 52%, respectively. The percentages of students in the experimental class who chose not to conform to the three question options of the learning initiative survey results were 24%, 9% and 8% less than those of the control class, respectively. Obviously, after adopting the teaching method proposed in this paper, the students in the experimental class showed a significant improvement in their English learning initiative than the students in the control class.

The results of the post-test findings on students’ learning efficiency in both classes are shown in Table 9. The results show that the students’ satisfaction with the results of the learning efficiency survey varied greatly with regard to the three questions: my concentration in English class, compared with the previous semester, I think my English learning efficiency has improved, and compared with the previous semester, I have made progress in listening, speaking, reading, and writing. Specifically, the percentages of students in the control class who chose to conform to the descriptions of the above three questions were 32%, 11% and 14%, respectively; the corresponding percentages of students in the experimental group who chose to conform to the descriptions of the above three questions were 53%, 47% and 54%, respectively. The percentage of students in the experimental class who chose average in the three question options of the learning efficiency survey results was 13%, 33% and 41% less than that of the control class, respectively. Obviously, after adopting the teaching method proposed in this paper, there is a significant increase in the learning efficiency initiative of the students in the experimental class compared to the control class.

Based on the above statistical results, this paper uses SPSS software to conduct independent samples t-test on the questionnaire post-test data. In the questionnaire, the scores represented by the three options are 1, 2, and 3, which shows that when the numbers are smaller, the interest, initiative, and efficiency of learning are higher. The statistical results of the independent samples t-test group for the post-test of the questionnaire are shown in Table 10. The results show that overall, there is a significant difference in the mean values of the nine questions related to students’ English learning. Therefore, summarizing the above analysis, it can be concluded that resource-sharing remote classroom teaching assisted by cloud computing technology for the experimental class can improve students’ learning interest, learning initiative, and learning efficiency.