# Cognitive Analysis of Pragmatic Functions of Discourse Markers in Spoken English in the Context of Computational Intelligence

Authors: Leping Huang; Yingfu Zhu
Journal: Journal of Environmental and Public Health
 (2022)

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## Abstract

To improve the level of oral English teaching, improve students’ oral communicative competence (OCA), and promote successful communication with native English speakers, this study studies the pragmatic function of dialogue markers and constructs the cognitive evaluation system of artificial intelligence (AI) by comparing the two cognitive evaluation systems of human subjectivity and knowledge base; in this study, the credibility of human subjective evaluation and the coupling degree of machine objective evaluation are discussed. The key coefficientR2 is obtained by the linear regression method, and the correlation is obtained by the Spearman correlation algorithm. The cognitive effects of knowledge base and AI are verified; the results show that the cognitive analysis system of pragmatic function of discourse markers in oral English based on AI has good teaching value. In the context of AI computing, we can put forward targeted learning methods and learning methods for students according to the amount and accuracy of markers in oral English that students have mastered, so that students can quickly improve the learning quality and the learning effect of oral English markers, which is more conducive to improving students’ oral English level and realizing students’ effective communication.


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## Body

## 1. Introduction

The primary function of discourse markers in spoken English is to communicate. Second, discourse markers can be used as an indispensable part of organizational language, to construct the context needed for interpersonal communication. The discourse meaning to be discussed is more coherent, vivid, and organized.In terms of pragmatic functions of spoken English discourse markers, because the types and specific forms of expression of discourse markers are relatively single and not diversified, and the pragmatic functions produced in different contexts are also different, there will be factors affecting credibility in the process of human evaluation. Wang Lu et al. in the corpus-based study on the expression patterns of cognitive positions in spoken Chinese academic English, markedness is a linguistic means for communicators to express their positions on the proposition with the help of lexical means, and it is an important resource for discourse construction [1]. In communication, the speaker can use markers to attract the listener’s attention, make the discourse go on normally, and coordinate the relationship between the speaker and the listener. Zhao Yan in the study of spoken English discourse markers is conducive to the communication between discourse markers and peers and constantly expands and extends the use direction and scope of discourse markers, to help improve students’ oral English expression ability [2]. In short, spoken English discourse markers can show the speaker’s emotion and attitude at that time. Qu Shifei put forward suggestions for English learners, especially those who regularly participate in international academic conferences, in the study of the default semantics of the discourse marker you know in oral English, pointing out the direction for more fluent English academic communication activities [3].In theoretical teaching and practical communication, students will be too rigid, mechanical, and inflexible in the use of discourse markers. It is also found that the use of discourse markers varies from person to person. Some students hardly use them, while others use them too frequently and use them indiscriminately. Chen Xinren et al. expended a lot of energy to study the use of discourse markers indicating causality by English learners [4]. Due to the influence of long-term examination-oriented education, Chinese students generally have strong written examination ability, but their oral expression and communication ability is very weak, commonly known as “dumb English.” Therefore, it is very urgent and necessary to improve Chinese students’ oral English expression ability. Liu Xian mentioned in the study that we should supplement and develop our oral English teaching level and analyze it to provide methods and references for improving the comprehensive level of oral English teaching [5]. Relevant studies have shown that the use of discourse markers can improve the oral coherence of Chinese English learners and maintain the relevance of the content. With the development of information science and technology, modern educational and teaching technologies and tools are widely used in English teaching. Liu Jun discussed the problems and suggestions faced by oral English man-machine dialogue teaching under the background of AI. Taking the practice of oral English teaching in Yinchuan as an example, he puts forward some suggestions on the application of AI in the process of oral English teaching [6]. In a word, AI plays a very important and key role in the field of English teaching. Hou Jing proposed the deep integration of AI technology and oral English teaching in the reform of oral English teaching mode in the AI era, which enriched oral English learning resources, innovated teaching methods, and expanded the learning environment on the basis, thus improving the teaching effect [7]. Zhang Xuehua et al. analyzed the problems of Chinese students using discourse markers in oral communication. Discourse markers are a natural language phenomenon of native English speakers, which is familiar but difficult to grasp for Chinese learners. The multiple pragmatic functions of discourse markers guide students’ oral English learning, cultivate students’ pragmatic ability of discourse markers, improve students’ oral English communication level, and cultivate knowledge-based skilled high-quality talents [8]. The application of artificial intelligence technology in oral English teaching optimizes the teaching methods and highlights the dominant position of students. By actively creating teaching situations, oral markers have developed from diversification to digitization, transforming traditional teaching into intelligent man-machine cooperative teaching, making oral personalization and autonomy, and further improving learners’ comprehensive oral ability.


## 2. Cognitive Status of Pragmatic Functions of Discourse Markers in Spoken English

### 2.1. Cognitive Evaluation System Based on Human Subjective Evaluation

Artificial subjective evaluation can clearly reflect objective problems, promote or hinder the development of objective things, and evaluate different subjective thoughts and personal emotions. The evaluation conclusion is still their own subjective thoughts, and people’s cognitive conclusions are often related to their own cognitive characteristics, and the so-called benevolent people see benevolence, wise people see wisdom is this truth. Based on the human subjective cognitive evaluation, everyone’s judgment of the nature and degree of things from their own perspective will directly affect personal coping activities and psychosomatic reactions. Therefore, it is necessary to change the relevant process of evaluation of cognition to a certain extent and carry out human subjective cognition evaluation more objectively.


### 2.2. Cognitive Evaluation System Based on Knowledge Base

The knowledge base is the product of the combination of traditional database technology and artificial intelligence technology. It is a continuous collection of declarative knowledge and process knowledge in a specific field. The knowledge base contains different abstractions or specific knowledge in a specific field. It is a clear description of the conceptualization of its knowledge base at the level of knowledge ontology. The knowledge base also pays more attention to the expression of terms and the relationship between terms at the conceptual level. At present, the general mode of knowledge base system is the three-level knowledge system of “fact + concept + rule,” and the three-level knowledge representation system is also developed on the basis of knowledge ontology. At present, the cognition of knowledge base is an important branch of AI technology system, one of the key contents of China’s national strategy for the development of AI and the key core technology for the transformation of various industries in traditional entity industries. However, in the process of transformation of relevant knowledge collection, due to many historical and other influencing factors, the development of traditional knowledge collection is difficult to meet the different needs of the current intelligent era, which also seriously hinders the process of intelligent transformation of the education industry. The cognitive evaluation analysis of the original knowledge base is shown in Figure1.Figure 1
Analysis of cognitive evaluation system of knowledge base.Figure1 shows the cognitive evaluation process of the application of traditional knowledge base in oral English teaching. Based on the cognition of the original text, it is divided into two parts: positive semantic base and negative semantic base. The corresponding positive score data and negative score data of semantic base are obtained from the semantic base, and the comprehensive score of the evaluation is obtained after the score data are comprehensively weighted and calculated by the experts.


### 2.3. AI-Based Cognitive Evaluation System

AI is the extension of the human brain. It not only has the same level of intelligence as human beings but also has the ability to learn, calculate, and judge alone. In the practical application of AI technology, it can replace human beings to complete most of the work in life. In the cognitive evaluations of AI, the cognitive intention, cognitive degree, and cognitive channel of different pragmatic functions in the teaching process within the scope of the evaluation system will be comprehensively evaluated and scored, and the most scientific, reasonable, and fair evaluation data results will be calculated by AI (Figure2).Figure 2
Analysis of the AI’s cognitive evaluation system.After the fuzzy evaluation of the original data is performed in the second round of cognitive data fusion, the results of the second round of fuzzy evaluation of the cognitive function are expanded in the second round of cognitive data fusion.


## 2.1. Cognitive Evaluation System Based on Human Subjective Evaluation

Artificial subjective evaluation can clearly reflect objective problems, promote or hinder the development of objective things, and evaluate different subjective thoughts and personal emotions. The evaluation conclusion is still their own subjective thoughts, and people’s cognitive conclusions are often related to their own cognitive characteristics, and the so-called benevolent people see benevolence, wise people see wisdom is this truth. Based on the human subjective cognitive evaluation, everyone’s judgment of the nature and degree of things from their own perspective will directly affect personal coping activities and psychosomatic reactions. Therefore, it is necessary to change the relevant process of evaluation of cognition to a certain extent and carry out human subjective cognition evaluation more objectively.


## 2.2. Cognitive Evaluation System Based on Knowledge Base

The knowledge base is the product of the combination of traditional database technology and artificial intelligence technology. It is a continuous collection of declarative knowledge and process knowledge in a specific field. The knowledge base contains different abstractions or specific knowledge in a specific field. It is a clear description of the conceptualization of its knowledge base at the level of knowledge ontology. The knowledge base also pays more attention to the expression of terms and the relationship between terms at the conceptual level. At present, the general mode of knowledge base system is the three-level knowledge system of “fact + concept + rule,” and the three-level knowledge representation system is also developed on the basis of knowledge ontology. At present, the cognition of knowledge base is an important branch of AI technology system, one of the key contents of China’s national strategy for the development of AI and the key core technology for the transformation of various industries in traditional entity industries. However, in the process of transformation of relevant knowledge collection, due to many historical and other influencing factors, the development of traditional knowledge collection is difficult to meet the different needs of the current intelligent era, which also seriously hinders the process of intelligent transformation of the education industry. The cognitive evaluation analysis of the original knowledge base is shown in Figure1.Figure 1
Analysis of cognitive evaluation system of knowledge base.Figure1 shows the cognitive evaluation process of the application of traditional knowledge base in oral English teaching. Based on the cognition of the original text, it is divided into two parts: positive semantic base and negative semantic base. The corresponding positive score data and negative score data of semantic base are obtained from the semantic base, and the comprehensive score of the evaluation is obtained after the score data are comprehensively weighted and calculated by the experts.


## 2.3. AI-Based Cognitive Evaluation System

AI is the extension of the human brain. It not only has the same level of intelligence as human beings but also has the ability to learn, calculate, and judge alone. In the practical application of AI technology, it can replace human beings to complete most of the work in life. In the cognitive evaluations of AI, the cognitive intention, cognitive degree, and cognitive channel of different pragmatic functions in the teaching process within the scope of the evaluation system will be comprehensively evaluated and scored, and the most scientific, reasonable, and fair evaluation data results will be calculated by AI (Figure2).Figure 2
Analysis of the AI’s cognitive evaluation system.After the fuzzy evaluation of the original data is performed in the second round of cognitive data fusion, the results of the second round of fuzzy evaluation of the cognitive function are expanded in the second round of cognitive data fusion.


## 3. Cognitive Problems of Pragmatic Functions of Discourse Markers in Oral English

### 3.1. Credibility of Subjective Evaluation

Artificial subjective evaluation is one of the basic viewpoints reflecting different cognitions in daily life. Artificial subjective evaluation and things cannot be measured, but can be “evaluated.” Subjective evaluation is scored and evaluated from the perspective of individuals, sometimes with personal emotional color, and sometimes one-sided and arbitrary. Therefore, the credibility of the final evaluation is not enough. In this case, we should consider the difference in credibility brought by human subjective evaluation. For the pragmatic function of spoken English discourse markers, because the types and specific forms of expression of discourse markers are relatively single, not diversified, and the linguistic functions produced in different contexts are also different, many factors affecting credibility will appear in the process of human evaluation. In the actual teaching process, it will affect the students’ lack of enthusiasm for the classroom, make the students lack interest in oral English learning, reduce the students’ enthusiasm, and attack the students’ self-confidence, so that the artificial subjectivity will affect the objectivity and fairness of the teaching evaluation system.


### 3.2. Coupling Degree of Machine Objective Evaluation

In spoken English, the frequency, quantity, and type of discourse markers or the needs of more complex pragmatic functions of dialogue markers are diverse. It is necessary to consider that discourse markers will produce different pragmatic functional effects in different contexts, which will also lead to different coupling degrees of pragmatic functional evaluation in oral practice. In this case, it is also necessary to strengthen the correct and rational use of discourse markers in the process of oral English. In the traditional English teaching, the teaching method is single, the students’ mastery and understanding of classroom knowledge are not comprehensive, and there is a lack of targeted teaching, which makes the students lack interest in oral English learning. In the application of oral English, they cannot correct the mistakes in time, which reduces the students’ enthusiasm and hits the students’ self-confidence. The application of AI technology in oral English can fully integrate and apply computational intelligence technology to oral English teaching and provide intelligent, personalized, and multi-style teaching methods in the teaching process, and students can experience and interact in the learning process according to their hobbies and needs. Therefore, when using machines to objectively evaluate different pragmatic functions in spoken English, we will more scientifically consider the evaluation data coupling analysis of relevant pragmatic functions in actual teaching. Intelligent machines can objectively and fairly score the information and data coupling evaluation results under different teaching methods, which is more conducive to the coupling development of education system evaluation in the future.


## 3.1. Credibility of Subjective Evaluation

Artificial subjective evaluation is one of the basic viewpoints reflecting different cognitions in daily life. Artificial subjective evaluation and things cannot be measured, but can be “evaluated.” Subjective evaluation is scored and evaluated from the perspective of individuals, sometimes with personal emotional color, and sometimes one-sided and arbitrary. Therefore, the credibility of the final evaluation is not enough. In this case, we should consider the difference in credibility brought by human subjective evaluation. For the pragmatic function of spoken English discourse markers, because the types and specific forms of expression of discourse markers are relatively single, not diversified, and the linguistic functions produced in different contexts are also different, many factors affecting credibility will appear in the process of human evaluation. In the actual teaching process, it will affect the students’ lack of enthusiasm for the classroom, make the students lack interest in oral English learning, reduce the students’ enthusiasm, and attack the students’ self-confidence, so that the artificial subjectivity will affect the objectivity and fairness of the teaching evaluation system.


## 3.2. Coupling Degree of Machine Objective Evaluation

In spoken English, the frequency, quantity, and type of discourse markers or the needs of more complex pragmatic functions of dialogue markers are diverse. It is necessary to consider that discourse markers will produce different pragmatic functional effects in different contexts, which will also lead to different coupling degrees of pragmatic functional evaluation in oral practice. In this case, it is also necessary to strengthen the correct and rational use of discourse markers in the process of oral English. In the traditional English teaching, the teaching method is single, the students’ mastery and understanding of classroom knowledge are not comprehensive, and there is a lack of targeted teaching, which makes the students lack interest in oral English learning. In the application of oral English, they cannot correct the mistakes in time, which reduces the students’ enthusiasm and hits the students’ self-confidence. The application of AI technology in oral English can fully integrate and apply computational intelligence technology to oral English teaching and provide intelligent, personalized, and multi-style teaching methods in the teaching process, and students can experience and interact in the learning process according to their hobbies and needs. Therefore, when using machines to objectively evaluate different pragmatic functions in spoken English, we will more scientifically consider the evaluation data coupling analysis of relevant pragmatic functions in actual teaching. Intelligent machines can objectively and fairly score the information and data coupling evaluation results under different teaching methods, which is more conducive to the coupling development of education system evaluation in the future.


## 4. Verification of the Cognitive Effect Based on Two Recognition Functions

### 4.1. Data Sources

The simulation data of the above algorithm is from the actual operation data of MATLAB software; the subjective data come from the subjective evaluation result data in the real person evaluation experiment.


### 4.2. Statistical Methods

In the cognitive evaluation of knowledge base under the background of human subjective evaluation and intelligence, it is necessary to use a variety of different basis function calculation formulas for the relevant data in the evaluation system, to better analyze the results of the two evaluation systems. First, the calculation formula of the fuzzy neural network is used to control the sixth-order polynomial depth iterative regression basis function of the recent change law of time-series data of the evaluation system, as shown as follows:(1)y=∑i=1n∑j=05Ajxij,where Aj coefficients are regressed of the j order polynomial, that is, each node in the function formula contains A0–A5 coefficients to be regressed; j is the polynomial order of the basis function.The second is the logarithm depth iterative regression function of the fuzzy neural network for statistical analysis of different information data in the evaluation, as shown as follows:(2)y=∑i=1nA⋅logxi+B,where A and B are the coefficients to be regressed; the meanings of other mathematical symbols are the same as those above; in the process of evaluation system data output, it is necessary to binarize the output data in turn and transform the logical data. The binary neural network algorithm is as follows:(3)y=∑i=1n1A+B⋅exi,where e is the natural constant; other mathematical symbols have the same meaning as formula (2); in the training of binary output data, if the data falling point is within the invalid interval of evaluation data, it is considered that the neural network has not fully converged. This model can judge the convergence degree of neural network training.When analyzing the key coefficients of cognitive evaluation data, it is necessary to apply the nonlinear curve estimation algorithm: determination coefficientR2:(4)R2=∑ixi−x¯∑ixi−x˜i,x¯=1n∑i=1nxi,where x¯ is the arithmetic mean of the investigated sample sequence, xi is the i input value in the sequence, and n is the number of investigation samples.When analyzing the correlation of information data, we need to use the Spearman correlation algorithm:(5)ρs=∑i=1NRi−R¯Si−S¯∑i=1NRi−R¯2∑i=1NSI−S¯21/2,where Ri and Si are the grades of the observed values, respectively; R¯ and S¯ are the average grades of variables x and y, respectively.


### 4.3. Result Analysis of Evaluation Coupling Degree

In the different cognitive evaluations of the pragmatic function of discourse markers in oral English, it is necessary to analyze the factors affecting the coupling and coordination of the two evaluation systems in the process of cognitive evaluation based on the practical data indicators of oral English application, to analyze the coupling results of different evaluation cognition. The comparative analysis of the coupling degree of two different cognitive evaluation systems is given in Table1.Table 1
Comparison of coupling degree of different cognitive evaluation systems.
GroupingKey coefficientRelevanceR2PρPKnowledge base algorithm0.8670.0060.8250.008AI algorithm0.9360.0010.9070.003To observe and compare the coupling degree data of two different cognitive evaluation systems more objectively, the data coupling results in Table1 are visualized, as shown in Figure 3.Figure 3
Visual comparison of coupling degree of different cognitive evaluation systems.Table1 and Figure 3 show the comparison results of the coupling degree of the evaluation cognitive system using the knowledge base algorithm and AI algorithm. The results clearly show that the key coefficients and correlation in the evaluation cognitive system under the application of the AI algorithm are higher than the evaluation cognitive coupling degree of the knowledge base algorithm, and the P values are less than 0.005. It is considered that the evaluation cognition system using AI technology can promote the coupling development of the evaluation system.


### 4.4. Result Analysis of Evaluating Credibility

Credibility is mainly analyzed and judged according to its legitimacy, authority, professionalism, standardization, transparency, and other factors. In the process of understanding the educational evaluation system, we also need to fully consider the connotation and influencing factors of credibility. For example, in the credibility evaluation index system, the analysis and comparison of key coefficients and correlation data are given in Table2.Table 2
Comparison of credibility of different cognitive evaluation systems.
GroupingKey coefficientRelevanceR2PρPKnowledge base algorithm0.8120.0080.8390.007AI algorithm0.9410.0020.9250.002According to the credibility comparison data of two different cognitive evaluation systems in Table2, Figure 4 is obtained.Figure 4
Visual comparison of credibility of different cognitive evaluation systems.Table2 and Figure 4 show the comparison of the credibility of two different knowledge evaluation systems. The R2 value of the key coefficient in the evaluation of the AI algorithm is significantly higher than that of the knowledge base algorithm ρ. The evaluation understanding of the AI algorithm in value comparison has also been significantly improved. Finally, the cognitive evaluation using the AI algorithm can scientifically and effectively evaluate the pragmatic function of discourse markers in oral English, improve students’ oral English level, and increase students’ overall self-confidence.


### 4.5. On the Cognitive Application of Pragmatic Function in the Process of English Teaching

With the rapid development of social education, the importance of learning English has become more prominent. At present, English has become the most widely used language in various fields. For example, to increase knowledge, broaden horizons, and work abroad, the scope of application of English is becoming more and more extensive. Qiu Huixiang holds that in English teaching, teachers should pay attention to cultivating students’ English application ability, especially students’ oral English expression ability. Teachers strengthen the interactive teaching method applied in oral English teaching, which helps to improve students’ oral expression ability. Moreover, the application of interactive teaching in oral English teaching breaks the teaching mode of “one speech hall,” advocates oral English teaching, carries out role-playing activities, and pays attention to the output of teaching results, to improve students’ oral English ability and level [9]. Therefore, more and more attention is paid to the shaping and comprehensive application of oral English in the teaching system. In students’ oral expression, it is found that students often cannot use discourse markers correctly, because the same discourse marker will have different meanings in different contexts, so the use of oral markers in oral English can better regulate the interpersonal relationship between both sides, clarify the relationship between the front and back discourse, maintain pragmatic balance, successfully show the speaker’s emotion and intention, and make the listener better understand the discourse, to achieve the purpose of harmonious communication. Regular reading enables students to more directly understand the function and grammar of oral markers in the context, enhance students’ awareness of table markers, and fully practice and master the usage of table markers. Oral English conversation practice between teachers and students and between students and students in oral English teaching is advocated. Students must be encouraged to dare to speak, be able to speak, and be able to speak. Students are guided to understand the role of discourse markers in conversation, discourse markers in the process of conversation are consciously used, and oral coherence is improved.In recent years, due to the rapid development of AI application technology in oral English teaching, AI information technology has been integrated into oral English teaching and students’ learning. Creating an oral communication atmosphere through human-computer interaction can strengthen the cultivation of students’ oral English and improve students’ interest in learning English. Long Gaoyan proposed that introducing AI technology into primary school information technology classroom teaching will help stimulate students’ interest in exploration, create more interesting teaching classes for students, and let students actively participate in the information technology teaching activities created by teachers [10]. It can be seen that improving students’ interest has a great impact on learning efficiency. Through the analysis of the data in the process of students’ learning, we can timely adjust the teaching strategy and optimize the teaching process. According to Hou Jing , the effective and deep integration of AI technology and oral teaching has promoted the reform of oral teaching mode, promoted the development of oral teaching towards intelligence, and achieved the purpose of effectively improving the effect of oral teaching on the basis of enriching oral learning resources, innovating teaching methods and teaching evaluation, and expanding the learning environment [7]. Lu Guoqing et al. classroom teaching behavior is an important factor affecting the effect of classroom teaching. The existing collection of classroom teaching behavior has shortcomings such as labor-intensive, fuzzy classification, and complex coding. AI technology provides a new opportunity for the accompanying collection of big data and automatic intelligent annotation of classroom teaching behavior [11]. In the context of AI computing, we can put forward targeted learning methods and learning methods for students according to the amount and accuracy of markers in oral English that students have mastered, so that students can quickly improve the learning quality and the learning effect of oral English markers, which is more conducive to improving students’ oral English level and promoting the development of the field of education as a whole.


## 4.1. Data Sources

The simulation data of the above algorithm is from the actual operation data of MATLAB software; the subjective data come from the subjective evaluation result data in the real person evaluation experiment.


## 4.2. Statistical Methods

In the cognitive evaluation of knowledge base under the background of human subjective evaluation and intelligence, it is necessary to use a variety of different basis function calculation formulas for the relevant data in the evaluation system, to better analyze the results of the two evaluation systems. First, the calculation formula of the fuzzy neural network is used to control the sixth-order polynomial depth iterative regression basis function of the recent change law of time-series data of the evaluation system, as shown as follows:(1)y=∑i=1n∑j=05Ajxij,where Aj coefficients are regressed of the j order polynomial, that is, each node in the function formula contains A0–A5 coefficients to be regressed; j is the polynomial order of the basis function.The second is the logarithm depth iterative regression function of the fuzzy neural network for statistical analysis of different information data in the evaluation, as shown as follows:(2)y=∑i=1nA⋅logxi+B,where A and B are the coefficients to be regressed; the meanings of other mathematical symbols are the same as those above; in the process of evaluation system data output, it is necessary to binarize the output data in turn and transform the logical data. The binary neural network algorithm is as follows:(3)y=∑i=1n1A+B⋅exi,where e is the natural constant; other mathematical symbols have the same meaning as formula (2); in the training of binary output data, if the data falling point is within the invalid interval of evaluation data, it is considered that the neural network has not fully converged. This model can judge the convergence degree of neural network training.When analyzing the key coefficients of cognitive evaluation data, it is necessary to apply the nonlinear curve estimation algorithm: determination coefficientR2:(4)R2=∑ixi−x¯∑ixi−x˜i,x¯=1n∑i=1nxi,where x¯ is the arithmetic mean of the investigated sample sequence, xi is the i input value in the sequence, and n is the number of investigation samples.When analyzing the correlation of information data, we need to use the Spearman correlation algorithm:(5)ρs=∑i=1NRi−R¯Si−S¯∑i=1NRi−R¯2∑i=1NSI−S¯21/2,where Ri and Si are the grades of the observed values, respectively; R¯ and S¯ are the average grades of variables x and y, respectively.


## 4.3. Result Analysis of Evaluation Coupling Degree

In the different cognitive evaluations of the pragmatic function of discourse markers in oral English, it is necessary to analyze the factors affecting the coupling and coordination of the two evaluation systems in the process of cognitive evaluation based on the practical data indicators of oral English application, to analyze the coupling results of different evaluation cognition. The comparative analysis of the coupling degree of two different cognitive evaluation systems is given in Table1.Table 1
Comparison of coupling degree of different cognitive evaluation systems.
GroupingKey coefficientRelevanceR2PρPKnowledge base algorithm0.8670.0060.8250.008AI algorithm0.9360.0010.9070.003To observe and compare the coupling degree data of two different cognitive evaluation systems more objectively, the data coupling results in Table1 are visualized, as shown in Figure 3.Figure 3
Visual comparison of coupling degree of different cognitive evaluation systems.Table1 and Figure 3 show the comparison results of the coupling degree of the evaluation cognitive system using the knowledge base algorithm and AI algorithm. The results clearly show that the key coefficients and correlation in the evaluation cognitive system under the application of the AI algorithm are higher than the evaluation cognitive coupling degree of the knowledge base algorithm, and the P values are less than 0.005. It is considered that the evaluation cognition system using AI technology can promote the coupling development of the evaluation system.


## 4.4. Result Analysis of Evaluating Credibility

Credibility is mainly analyzed and judged according to its legitimacy, authority, professionalism, standardization, transparency, and other factors. In the process of understanding the educational evaluation system, we also need to fully consider the connotation and influencing factors of credibility. For example, in the credibility evaluation index system, the analysis and comparison of key coefficients and correlation data are given in Table2.Table 2
Comparison of credibility of different cognitive evaluation systems.
GroupingKey coefficientRelevanceR2PρPKnowledge base algorithm0.8120.0080.8390.007AI algorithm0.9410.0020.9250.002According to the credibility comparison data of two different cognitive evaluation systems in Table2, Figure 4 is obtained.Figure 4
Visual comparison of credibility of different cognitive evaluation systems.Table2 and Figure 4 show the comparison of the credibility of two different knowledge evaluation systems. The R2 value of the key coefficient in the evaluation of the AI algorithm is significantly higher than that of the knowledge base algorithm ρ. The evaluation understanding of the AI algorithm in value comparison has also been significantly improved. Finally, the cognitive evaluation using the AI algorithm can scientifically and effectively evaluate the pragmatic function of discourse markers in oral English, improve students’ oral English level, and increase students’ overall self-confidence.


## 4.5. On the Cognitive Application of Pragmatic Function in the Process of English Teaching

With the rapid development of social education, the importance of learning English has become more prominent. At present, English has become the most widely used language in various fields. For example, to increase knowledge, broaden horizons, and work abroad, the scope of application of English is becoming more and more extensive. Qiu Huixiang holds that in English teaching, teachers should pay attention to cultivating students’ English application ability, especially students’ oral English expression ability. Teachers strengthen the interactive teaching method applied in oral English teaching, which helps to improve students’ oral expression ability. Moreover, the application of interactive teaching in oral English teaching breaks the teaching mode of “one speech hall,” advocates oral English teaching, carries out role-playing activities, and pays attention to the output of teaching results, to improve students’ oral English ability and level [9]. Therefore, more and more attention is paid to the shaping and comprehensive application of oral English in the teaching system. In students’ oral expression, it is found that students often cannot use discourse markers correctly, because the same discourse marker will have different meanings in different contexts, so the use of oral markers in oral English can better regulate the interpersonal relationship between both sides, clarify the relationship between the front and back discourse, maintain pragmatic balance, successfully show the speaker’s emotion and intention, and make the listener better understand the discourse, to achieve the purpose of harmonious communication. Regular reading enables students to more directly understand the function and grammar of oral markers in the context, enhance students’ awareness of table markers, and fully practice and master the usage of table markers. Oral English conversation practice between teachers and students and between students and students in oral English teaching is advocated. Students must be encouraged to dare to speak, be able to speak, and be able to speak. Students are guided to understand the role of discourse markers in conversation, discourse markers in the process of conversation are consciously used, and oral coherence is improved.In recent years, due to the rapid development of AI application technology in oral English teaching, AI information technology has been integrated into oral English teaching and students’ learning. Creating an oral communication atmosphere through human-computer interaction can strengthen the cultivation of students’ oral English and improve students’ interest in learning English. Long Gaoyan proposed that introducing AI technology into primary school information technology classroom teaching will help stimulate students’ interest in exploration, create more interesting teaching classes for students, and let students actively participate in the information technology teaching activities created by teachers [10]. It can be seen that improving students’ interest has a great impact on learning efficiency. Through the analysis of the data in the process of students’ learning, we can timely adjust the teaching strategy and optimize the teaching process. According to Hou Jing , the effective and deep integration of AI technology and oral teaching has promoted the reform of oral teaching mode, promoted the development of oral teaching towards intelligence, and achieved the purpose of effectively improving the effect of oral teaching on the basis of enriching oral learning resources, innovating teaching methods and teaching evaluation, and expanding the learning environment [7]. Lu Guoqing et al. classroom teaching behavior is an important factor affecting the effect of classroom teaching. The existing collection of classroom teaching behavior has shortcomings such as labor-intensive, fuzzy classification, and complex coding. AI technology provides a new opportunity for the accompanying collection of big data and automatic intelligent annotation of classroom teaching behavior [11]. In the context of AI computing, we can put forward targeted learning methods and learning methods for students according to the amount and accuracy of markers in oral English that students have mastered, so that students can quickly improve the learning quality and the learning effect of oral English markers, which is more conducive to improving students’ oral English level and promoting the development of the field of education as a whole.


## 5. Summary

Based on the cognitive analysis of the pragmatic functions of different discourse markers in spoken English, this study constructs the cognitive evaluation system of the knowledge base of human subjective evaluation in spoken English and the cognitive evaluation system under the application of AI technology. Through statistical calculation, the cognitive effects of the two evaluation and recognition functions are compared and verified, and the comparison results of the coupling degree of the evaluation and recognition system and the reliability of the evaluation system are verified. The results show that the evaluation and recognition system under the background of artificial intelligence technology has better key coefficient values and correlation values. Creating an oral communication atmosphere through human-computer interaction can strengthen the cultivation of students’ oral English and improve students’ interest in learning English. It is an innovation and optimization of the previous traditional oral evaluation methods. It can scientifically and effectively evaluate the ability of oral English and comprehensively promote the development and evaluation of English core literacy.



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