Abstract
In order to improve the online oral English teaching model, enhance its efficiency, and widen its utilization in university teaching, this study developed a rigorous oral English teaching model, constructed a comprehensive oral English evaluation model, and used PBL to guide the direction of oral English curriculum under the influence of big data-driven background. The use of online oral English teaching mode was realized to ensure that students can internalize knowledge in class. A complete teaching method was used to ensure that learners can master more knowledge in English class, promote the operation of online English learning in the network environment and create a safe learning environment for students, improve students’ oral English ability, improve oral teaching, and improve the integrity of oral teaching. The deepening of big data and online review teaching will be the requirements of the future teaching environment. Therefore, it was decided that only by making full use of all the advantages of information technology, combined with the background of education and teaching, can we explore the new education and training methods needed in the new semester.
1. Introduction
With the development of English teaching in higher vocational colleges, some achievements have been made in English teaching. According to the 2020 College English Teaching Survey, the adoption rate of English teaching in my country is gradually accelerating. Some colleges and universities offer English courses through online tutoring, accounting for 46.8% of colleges and universities. This shows that there are different types of online English teaching. With the improvement of people’s quality of life, oral English teaching has gradually become popular. The teaching mode is shown in Figure 1. However, some higher vocational colleges are lagging behind the teaching model. Due to the different economic development benefits in various cities, a few rural colleges do not have supporting online equipment. However, for a long time, the teaching team has been affected by traditional courses, which leads to the need to strengthen English teaching, and there are some setbacks in oral English teaching, which requires further research [1]. At this stage, in order to improve students’ learning ability in French-English classrooms, it is necessary to have a certain understanding of language, literacy, and basic English work teaching. Assessment is based on students’ ability to convert English to avoid situations where students “do not speak” in class. At the same time, because the oral pronunciation of some higher vocational college teaching staff is not standard, the students are influenced by teachers for a long time, so they cannot guarantee the English pronunciation without regional characteristics. Therefore, the reasonable application of modern mobile information technology can be ensured by establishing evaluation indicators of college oral English, measuring the weight of evaluation indicators of oral English, and setting up comprehensive English evaluation model. In order to ensure the rational use of today’s mobile data, demonstrate the strength of learning and work in the context of big data, win the interest of college teachers, and make students more effective [2]. Online oral English teaching is a hot spot that we need to pay attention to. Connecting to the Internet can not only benefit student growth, but also support teaching improvements and improve student achievement. Therefore, online oral English teaching occupies a certain position in the educational environment and we can analyze it from a macroperspective. Today, modern information continues to develop, combining learning work with modern technology to complete the results of the current English-speaking social measurement. Students can learn the basics of English courses through teaching websites such as WeChat and Weibo [3]. Due to the high frequency of mobile devices used by students, online English teaching is more flexible, real-time, and ubiquitous, which promotes this teaching method to better play its function. Driven by big data, online English teaching can use WeChat and the Internet to broadcast audio, English, photos, and videos in real time, and network equipment to complete oral English teaching, master the state of oral English teaching, and recognize the driving force of big data. This proves the driving effect of big data and applies the blended teaching method to ensure that students can better interpret English textbooks after the course, help teachers get better feedback from students, improve current communication standards, and improve the overall quality of online English teaching. In addition, this online English teaching model also follows the same model in improving modern teaching and should face some difficulties at this stage [4]. Therefore, this paper can explain the big data-driven online English teaching through a combination of thought and practice and examine it from a visual point of view. The development of English teaching and the information age can be stabilized so as to carry out targeted research.
2. Literature Review
Roslim et al. noted that, in the present stage’s network environment, oral English teaching methods are embracing a comprehensive innovation; a variety of new teaching methods have been all over the whole teaching environment [5]. Jia thought that it can be analyzed based on the previous oral English teaching to understand students’ personal characteristics and learning advantages and master the differences, advantages, and disadvantages between traditional classroom teaching methods and online oral English teaching methods. In this way, the teaching content can be optimized and students’ enthusiasm for English courses can be aroused. For example, in the way of flipped classroom, online equipment is used to send English learning materials to students’ mobile phones in advance, to ensure that students can solve more oral problems in a limited time through teacher’s arrangement and self-reading. Students can be encouraged to interpret their own English through personalized reading methods so as to ensure the improvement of teaching efficiency to interpret their own English through personalized reading methods and ensure the improvement of teaching efficiency [6]. Yan and others said that English class is not always the first in the class. The disadvantage of traditional English education lies in that teachers need to check each student’s oral English learning status, measure what students have mastered by means of evaluation, and master students’ recent learning status by means of classroom questioning and examination. However, due to the limited number of outstanding teachers in the school, it is impossible to ensure that every teacher can grasp the focus of oral English teaching. As a result, students are unable to self-regulate and self-manage. It is difficult to help more students master oral English and guarantee the enrollment rate only by teachers’ own efforts in class [7]. Watson et al. thought that online oral English teaching can avoid this problem and effectively record what students have learned in class. Teachers only need to combine network resources to create a platform for effective communication with students and create a positive learning environment for students. Students can be enabled to plan out the center of classroom operation, which can motivate students to learn independently and master new knowledge in the network environment and effectively improve classroom problems and enhance students’ interest and motivation in oral language learning [8]. Hayles evaluating English language teaching in high-performing universities, because teaching is influenced by natural thinking, teachers are the key figures in the classroom and they teach the course content through cramming, so students do not have the ability of English expression and there is often a passive learning way [9]. Connolly-Barker et al. developed online oral English teaching plan based on big data, which encouraged students to improve their interest in English courses through this way of learning and avoided the hassle of smart teachers and students on the podium not looking at anything in the classroom. At the same time, improving the teaching process can create an environment for oral communication, improve the quality of oral English, and expand cultural English. The application of big data-driven data accumulation method in English language teaching can ensure the complete application of massive data in oral English teaching. To ensure the integrity of online oral English teaching links of the text, the statistical methods were used to ensure better integration of similarities between different texts. Students’ career was well planned so as to ensure the similarity of online oral English teaching links of the text [10]. In addition, Keane et al. made directional analysis on the daily English learning status of students in higher vocational colleges. In this way, various results can be guaranteed to be aggregated in the table by means of text collection so as to understand the current situation of oral English teaching in vocational colleges. According to the analysis of its current situation, weak areas in this aspect can be grasped so as to ensure that college students meet the needs of social recruitment [11]. According to the current survey of oral English teaching, Guan et al. derived the perception that the English basic ability of the newly enrolled students is relatively weak, their vocabulary is small, and they do not have English communication ability. Besides, there are certain deficiencies in grammar knowledge, which will increase the difficulty of teaching English teachers. The application of data driven by big data can help teaching staff to know with which module students have problems and ensure that students will not be nervous in class by means of census or calculation [12]. Hai helped the students to master oral English knowledge anytime and anywhere through online teaching. Through the teaching method of network channel, the strong accent problem of English teachers can be alleviated to avoid the inaccurate oral pronunciation of students during the English defense. On the other hand, with regional characteristics and other problems using diversified teaching methods, the application of computer technology, cloud computing, and other advanced technologies were used to set up edge computing English model. In this way, students could have a good knowledge of classroom grammar and do not have to recite words only on the basis of a single sentence vocabulary, but to master all the contents of oral English teaching through comprehensive understanding. The teaching concept was broken so as to ensure that the comprehensive quality and ability of each English teacher in higher vocational colleges can be improved accordingly, so that they have certain classroom control ability and there is no need to carry out follow-up work through cramming teaching [13]. Qiao et al. used online teaching methods to increase the recruitment of foreign teachers. They used remote equipment to reduce the influence of space time and other interference factors, ensuring that there is more regional curriculum teaching work for Chinese teachers to complete. The interaction between teachers and students was comprehensively promoted so as to ensure that the course time is reasonably arranged and the oral English curriculum is more reasonable and complete. There was no shortage of class arrangements; teaching conditions were able to be up to standard. More time was spent teaching oral English through online equipment so as to avoid the influence of external interference factors on students [14].
3. Research Method
3.1. Develop Rigorous Oral English Teaching Model
According to the current situation of college students, increasing the use of data-driven data and the process of isomorphic data clustering are guaranteed to be useful for online English courses. The big data set teaching mode and the supporting role of the virtual teaching classroom are shown in Figure 2.
First of all, we can analyze the massive big data on campus to find the similarity in English texts. The method of comparison between different texts was used to obtain English data and understand the similarity between feature words. For example, by comparing text 1 with text 2 in spoken English in a modern way, we can find the characteristic words according to the whole paragraph. If the same problem exists in the feature words, the comparison method can be used to determine the comparison value of 1. Otherwise, it can be programmed to 0 and that can be done by summation to understand the feature words whose comparison result is 1 in online spoken application. Online simulation driving by means of text similarity calculation can be carried out [15]. The English text indicates that it can be planned as , , and can be expressed as the number of English texts and English features. corresponds to the number of English features of text ; at last, can be planned as basic feature words of text ’s, promoting and to be able to compare correctly, which is convenient for the subsequent interpretation of English semantics. Formula (1) can be obtained:
Secondly, according to the operation mode of online English teaching, the content in the big data text is decomposed. The intercommunication between texts was used to ensure that the feature words in the same text are selected. By comparing all the features of in the oral text, the cumulative value can be obtained, as shown in formula (2):
In the next stage, feature words between the same spoken text can be edited in a big data-driven way; the differences between the two was grasped. The total number of feature words in the text and was known; in this way, the work of knot accumulation between each other can be carried out smoothly and the comparison result can be calculated as shown in formula (3):
In online English teaching, there are some differences between different texts and they can be edited according to morphemes and meanings of the same feature words. If all the features in of are performance, it can be corrected according to the total amount of English feature words, that is, to control the similarity between texts. The similarity between texts can be obtained through calculation, as shown in formula (4):
In addition, all contents of online oral English teaching can be edited by heuristic clustering method. Big data can be used to implement clustering and the starting point of online teaching is selected as a specific indicator of English teaching. In this way, English content can adapt itself in the system to ensure the integrity of clustering index. Through the calculation method of fixed threshold value, the text obtained in higher vocational colleges is edited to understand the text similarity in the same region (the teaching mode of spoken English in network environment and the teaching mode of spoken English in vocational colleges). Ensuring the evaluation index is consistent with the actual clustering results which can guarantee the smooth development of text collection. On this basis, the set of texts is realized to ensure the establishment of a complete evaluation system on the corresponding indicators, which is convenient for teaching staff to have a more intuitive understanding of students’ current learning status [16]. The next step is to grasp the characteristics of A in the matrix and find the obtained solution by means of hierarchical sorting and consistency test, find out the obtained solution, use the unified processing method, and master the specific content in the same level and the upper level. In this way, the elements can be arranged to determine the specific values of the data in the matrix. For example: integrate all contents of online English teaching by means of weight ranking result determination, control the critical index value, and set it as CI. According to the editing mode of critical index value, the corresponding random sequence number is found. Using random acquisition method, the consistent result is CR. At this point, when CR is less than or equal to 0.1, the consistency of online oral English teaching can be mastered through single ranking [17]. At the same time, the application of matrix element values should be strengthened and the consistency test formula should be written, as shown in formulae (5)–(7):
It can be seen that the consistency index and various dimensions between matrices in oral teaching texts ensure that random indexes in RI and CR will not cause problems. By means of average randomness, grasp the index results involved in other aspects and strictly comply with the above formula requirements and procedures. In this way, students in higher vocational colleges can learn the content that has not been explained in class through online oral English teaching. Learn the content not explained in class and control the weight value of each indicator so as to pass the consistency test. Let students learn English teaching content better in the classroom to ensure the integrity of the comprehensive evaluation method [18]. Master the leading factors in online oral English teaching and plan them out by means of index set evaluation as . The weight set corresponding to the specific index set is calculated and r is summarized as a uniform weight value. In this way, the accuracy of the evaluation result set can be guaranteed and the fuzzy evaluation matrix of the evaluation result set can be calculated as formula (8):
As r usually exists as the main factor, its basic index can be controlled. is planned as the membership degree of the test link of oral English text, so as to control the evaluation factors. can be used as the basic comment and control the measurement probability in the calculation method, which can be presented in the form of a single factor. Set the evaluation index and plan within the number of comments, from which formula (9) can be obtained:
The fuzzy subset of the membership vector unit is B, so a comprehensive evaluation result can be calculated. By means of comprehensive evaluation, the specific content of the evaluation matrix is mastered. The calculation integrity of each evaluation and valuation are also guaranteed and the operation mode of the matrix is determined. According to the parameters of the target layer, it can be concluded that the evaluation result set in spoken English is , which is taken as the interpretation value of membership vector b. In this way, the reduction can be carried out according to the maximum membership value to ensure the correctness of the evaluation results, from which formula (10) can be calculated:
Thus, the basic learning status of college students in English teaching class can be obtained to ensure that this model, namely, formula calculation, can show the current learning status of students in higher vocational colleges. Take the students in a school as the research object so as to use the way of questionnaire survey. The number of questionnaires should be controlled to 1500. In this way, five of them can be selected as the presentation form of evaluation results to promote contemporary college students to better grasp the knowledge of oral English. The method of recycling questionnaire was used to grasp the content represented in the model at all levels [19]. At the same time, we can divide oral English teaching according to the current development of the form. Driven by big data, students’ current oral English learning status is evaluated to ensure that there will be no doubt in the classroom. Let the teaching staff pay more attention to the network data management platform and ensure that the evaluation method of the model in this paper is more complete, so as to broaden the scope of students’ current classroom learning. In this way, the massive data in the network system can be well controlled, the contents of all kinds of big data can be aggregated, the frequency of false positive can be reduced, and the false negative ratio can be lower than 1% [20]. At the same time, given the current teaching of English in the classroom, there is a need to increase the use of large-scale research data and to develop strict hierarchical control strategies. According to the learning status of students in this school, their daily grades, classroom grades, and final grades are shown in Table 1.
In this way, the integrity of online courses can be ensured and teachers can use PBL teaching model, flipped classroom model, and other related online teaching methods. Integrate the contents of oral English teaching, such as text, audio, and pictures, so as to mobilize students’ enthusiasm for the course. Use the way of group cooperation to complete the follow-up course, guided by the teacher, to ensure that it plays an inspiring leading role.
3.2. Online Teaching Framework
In the process of one-to-one digital teaching in class, teachers allow and require each student to carry mobile phones to participate in classroom teaching activities. After the analysis of each student’s learning grid and learning characteristics, teachers can set up relevant classroom inquiry questions according to each student’s learning situation of preclass learning materials. Through the classified sending function of WeChat public platform, the study project or problems that students need to solve will be pushed to each student’s mobile phone. Students can either finish the task independently or set up a study group with similar learning level and style designated by the teacher. During this period, teachers can observe students’ learning progress, timely answer students’ questions and doubts, and also carry out constructive dialogue and argumentative communication with students on a specific problem, through the cooperation of teachers and students to complete the project task or solve the problem, so as to effectively promote students’ knowledge internalization [21]. The implementation framework for this pattern can be shown in Figure 3.
Although humans have different meanings and interpretations of the concept of teaching type, they have simple signs and considerations for teaching modes, as shown in Table 2.
Generally speaking, a complete teaching model should include five main parts: theoretical basis, teaching objectives, operating procedures, teaching environment, and teaching measures [22]. Among the five themes, emotional teaching or ideological teaching is the basis and basis for designing teaching, and plays a role in other concepts; the curriculum goal is the basis of the curriculum, which restricts the curriculum setting, restricts the combination of teachers and students, and restricts the curriculum, but also a measure of standards and teaching; workflow is the connections and steps that follow standards; teacher-student blending is the curriculum that prepares teachers and students for professional learning; instructional methods and environments that maximize the effectiveness of instructional standards; instructional assessments that inform achievement of instructional goals level and refine teaching procedures, teacher and student responsibilities, etc., to change and further improve the type of teaching [23]. Generally speaking, any teaching model will contain these elements, as for the specific content of each element, because of the different teaching model. By analyzing the structure and elements of teaching mode, we can get such a revelation: it is not only difficult to carry out, but also one-sided and narrow to separate all the elements of teaching model and study and apply them in isolation [24]. In order to further indicate the relationship between various elements, this study attempts to carry out visual representation of them on the basis of comprehensive studies, as shown in Figure 4.
4. Conclusion and Analysis
There are many methods to construct the teaching model, such as theoretical deduction, empirical induction, and the corresponding principle of reference and innovation. Based on the existing mobile social network teaching model, this study modifies classroom teaching and conducts theoretical measurements on the selection and application of online classroom teaching from the aspects of concepts,principles, the terms come down, and elements, based on the structural elements of the teaching model that includes theoretical foundation, teaching objective, operational procedure, teacher-student interaction, teaching environment, and teaching evaluation, the feasibility and convenience of mobile social network as flipped teaching platform are fully considered. The essence of flipped classroom teaching model is the stage characteristics of teacher-student interaction behavior and knowledge learning is the main line [25]. Based on the three-layer concept of “preclass knowledge exchange + classroom experience + after-class knowledge exchange,” through the reunderstanding of online classroom teaching practice in colleges and universities, more knowledge, more evaluation, and more expansion and refinement, the process has been perfected, stable, and regular, forming the theoretical model of online courses as shown in Figure 5.
On the contrary, it should be said that the model fits well with the structural system and element connotation of a complete teaching model. The theoretical basis module, as the primary basic supporting element of the whole teaching model, directs and standardizes other elements. It should be clear that this theoretical basis is mainly combined with the student-centered teaching concept upheld by flipped classroom. This concept emerges as a self-contained set of macroguiding theories, such as Humanistic Learning Theory, Constructivist Learning Theory, Blended Learning Theory, and Mastery Learning Theory. It is also detailed into the specific microoperation strategy theory of all levels and links, including socialized learning theory, network learning community theory, teaching design theory, activity learning theory, and inquiry learning theory, which run through the whole process of teaching [26]. Extensive assessments include all the activities of teachers and students who can write lessons that can be used as assessments to control teaching and learning. With the continuous improvement of information technology, various advanced software makes this information widely available in files, such as audio, video and audio, photos, videos, and so on, as shown in Figure 6. Figure 6 shows details of student-level learning through cloud learning in classroom software. As can be seen from Figure 6, the student’s learning status is good in February and March, declining in early April, reaching the peak in mid-April, and declining rapidly in May. In addition to the above comparison, the platform also includes activity participation comparison attendance statistics, database view data records, etc. Figures 6 and 7 show that the students are active in classroom interaction at the peak of experience value, but not active at the trough [27].
Therefore, according to the data of different periods, teachers can understand the overall situation of students’ learning in a certain period so as to plan and adjust for the next stage of teaching. Data can be used to warn students about their learning. Finally, the proportion of data can be allocated to form the final score.
Electronic data focuses on the process of learning and can influence how students learn as they learn. By comparing the vocational training scores of different time periods, the performance of students can be seen. The teaching teacher and teaching assistant made statistics on each student’s weekly self-evaluation score and the score given by the teacher and calculated the average score of weekly student evaluation and teacher evaluation, as shown in Figure 8.
According to the comparison in Figure 8, it is not difficult to see that students’ and teachers’ ratings on students’ works in electronics files generally show an upward trend. Students’ self-evaluation results have experienced a change from blind confidence to solid foundation and then to leap upgrade, which is the benefit of electronic files for students. Teachers’ scores showed a gradual upward trend on the whole and they were slightly dissatisfied with students’ scores at the beginning. This is mainly caused by the fact that students have not mastered this learning mode, and the students’ self-evaluation and teachers’ evaluation show a gradually close trend; it also shows that the model can help students and teachers [28]. With the certain changes in the teaching methods of higher vocational colleges, students can be guided through PBL courses, allowing students to use the Internet to promote learning in classroom teaching and answer questions according to questions. The teaching video is taken as the main application subject to help students to carry out follow-up courses in the form of carrier, which can change the focus of teaching work. Students in the teaching work no longer through a single knowledge point to explain the follow-up work, but through a certain point of view. The combination of PBL teaching mode and flipped classroom can increase the advantages of students in oral English teaching, to ensure that the contents of vocational colleges are applied to oral English teaching so as to ensure that the traditional teaching methods no longer exist. The utilization of Internet video viewing, teaching structure integration, and group class discussion methods can allow teachers to solve the students’ problems in the classroom through the network equipment. The application of online oral English teaching methods, such as WeChat public account, Weibo, class and network class, is added to generate such comprehensive evaluation method after class so as to guide students in the direction of oral English course. The construction of teaching mode is shown in Figure 9.
In this paper, oral English evaluation model, network environment oral English index measurement, teaching evaluation system, and teaching list setting method were constructed. In the limited time, students could be taught more knowledge so that oral practice work was distributed in the network environment. Students could have more time to learn spoken English and apply it to their daily life, so as to avoid the constraints in English class. The second is to ensure that the policy of improving the information system is implemented, and teachers can still understand the current network system courses that students are learning. Establish the correct oral English classroom development goals for students through big data analysis and it can be combined with historical data diagnosis. Understand students’ deficiencies in learning methods, daily life, and English class. Through the Chinese targeted response measures, teachers can be ensured to master the current situation of students from mass data. Students’ discerning ability can be improved and materials are used to verify oral English teaching work. Through the data of different dimensions in oral English teaching, present students’ performance skills and emotional values, and increase the feedback mechanism. The above two tables are used to strengthen teachers’ guiding ability and questioning ability, so that students can provide corresponding feedback in time through the network environment. The data generated from our big data reflects the current state of English teaching in higher education institutions. As the driving force of big data, oral English teaching will definitely undergo corresponding changes, to ensure that teachers can identify the actual status of students in teaching activities through feedback information in the online environment. Use the method of analysis, judgment, and evaluation to let the students know their lack of spending in the teaching plan. Use data collection processes to ensure students and teachers can measure English language proficiency in an online environment. Stabilize interaction between students and teachers, use research skills to ensure the application of student learning strategies, and enable students to use online English language courses and have enough time in English class. The use of network equipment to evaluate this course is convenient for the follow-up summary work. Students develop accurate learning goals, which are necessary to further develop online English language teaching. Based on the above analysis, in the process of determining the teaching objectives of the online classroom teaching type, we can first divide the teaching objectives into various levels of knowledge from low to high according to the intellectual process. Distinguish the private sector, such as practical skills, theoretical understanding, etc., and provide students with reference in advance by showing video or graphic materials in advance, so that students can learn independently and share knowledge. In class and after class, students should focus on deep learning of higher-order thinking ability and cognitive content, including application analysis and comprehensive innovation so as to realize knowledge internalization and knowledge transfer. Therefore, this study aims to conduct a more detailed study of knowledge in classroom courses, as shown in Figure 10.
Regarding the meaning of holistic learning, since the distribution of goals only has a wide range of knowledge, ideas, and skills, the importance of the whole and study habits is ignored. Die can refer to four target distributions, along with progress. The three types of teaching, classroom experience, and the hierarchical characteristics of after-class knowledge exchange finally form a complete curriculum with appropriate teaching objectives for online classroom teaching, as shown in Figure 11.
5. Conclusion
In order to achieve the overall improvement of students in higher vocational colleges, this sentence provides an idea of expectations, starting from the implementation of online English teaching, and from the direction of big data analysis, operation, and supervision. Let students learn spoken English online and increase the use of spoken English in real life. Since language and culture are related to each other, culture is derived from language. Therefore, oral English can be planned as the main part of the English curriculum. Through the big data-driven approach, students can better master the English teaching content outside the classroom and ensure that students can better understand the culture. In order to be driven by big data, students can correctly master the cultural knowledge in the language through online oral English teaching. At the level of books, students’ oral English learning channels are broadened to ensure that they can master the objective reasons in English teaching. The oral English summary sentence pattern, word, and culture can be refined. The oral English teaching list can be arranged for students through the way of learning video materials in order to ensure that the subsequent students can use the time in and out of class to better absorb and understand oral English knowledge.
Data Availability
No data were used to support this study.
Conflicts of Interest
The authors declare that there are no conflicts of interest with any financial organizations regarding the material reported in this manuscript.
Acknowledgments
This work was supported by the Education Reform Program of Xi’an University of Technology (xjy2048) and Special Scientific Project of Education Department of Shaanxi Provincial Government (20JK0252).