# Teachers’ Talk and Students’ Attitude in Learning English at Secondary Schools in Indonesia: A Correlational Analysis

Authors: Nasmilah Nasmilah
Journal: Education Research International
 (2023)

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## Abstract

Teachers’ talk is a key tool for engaging students in learning. This research is carried out to examine the correlation between students’ attitude and teachers’ talk in learning English at secondary schools in Indonesia in which English is taught as a foreign language. These two aspects are substantial when learning objectives are to be achieved. Unlike the previous studies that focused more on the nature of teachers’ talk and its effect on the development of specific language skills, this study scrutinizes the interrelation between teachers’ talk and one of the individual differences possessed by language learners, i.e., attitude. Two research questions are formulated to be answered by this study. First, what types of attitude possessed by the learners and how they are classified? Second, how does teachers’ talk correlate with the learners’ attitude in the attempt of achieving the learning goals? A quantitative approach is applied in this study involving learners of two secondary schools, namely, SMAN 6 and SMKs Harapan Bangsa as research population in Sidrap, a district in South Sulawesi Province, Indonesia. The researcher used purposive sampling technique in choosing 160 research samples from these two secondary schools. The data were collected using questionnaire and a set of hypotheses testing is utilized to measure the normality, linearity/regression, validity, and the reliability of the instrument. Pearson correlation value formula by Pearson was used to determine whether correlation existed between teachers’ talk and students’ attitude in learning English. The result of the analysis shows the correlation coefficient between teachers’ talk (X) and students’ attitude (Y), which confirms that both variables have correlation, which was interpreted as fair. The Pearson correlation value, which in this case is 0.350, shows a tenuous positive correlation. The implication of this study lies on the importance of teachers’ talk in shaping the learners’ attitude, which in turn leads to the success of language learning.


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## Body

## 1. Introduction

One of the interesting topics for discussion in educational settings is teachers’ talk (TT). It is one of the teaching strategies that the instructor uses during the teaching and learning process [1, 2]. Instructor’s talk is described by Xiao-yan [3] as “the kind of language used by the teacher for instruction in the classroom.” The TT is beneficial for the students because it made it easier for them to comprehend the content that was being covered. Through the teacher’s conversation in class, the students learn the lessons taught. When speaking with pupils in language classroom, whether they are beginning, advanced, or intermediate level in the target language, teachers take into account their own types of language. However, the language the instructor uses might have impact on the target language to help them achieve their objectives [4–7]. This implies that the language used when teaching should be appropriate with the levels of learners’ performance in specific classroom contexts. For the purpose of enhancing pupils’ abilities, the instructor employs language that can help them achieve their objectives. Students practicing the target language in class are crucial in the process of learning a language. Tsegaye and Davidson [8] stated that in communicative English as a foreign language (EFL) classes, students need much opportunity to practice the target language, so the teacher should reduce the amount of their talk to 20%–30% of the classtime, and students’ talk time should be around 70%–80% during the lesson time. Language learners require longer time to understand and comprehend the course material. The benefits of teacher discussion were predicated on the equal distribution of time between teacher and pupils that it provides. The students’ capacity to speak was improved if the teacher cut back on the amount of time he or she talked in class [9–12]. There was a claim that excessive instructor talking time could even lower students’ motivation to speak up [13]. The primary setting where students of foreign languages are frequently exposed to the target language is the classroom. TT refers to the language that teachers use to instruct students in a classroom. This phrase is defined as “that variety of language sometimes employed by teachers when they are in the process of teaching (Longman Dictionary of Language Teaching and Applied Linguistics).” Teachers frequently simplify their speech in an effort to communicate with students, giving it many of the traits of foreigner talk and other reduced speech geared to language learners [14]. Ellis [15], who has researched second language acquisition for a long time, has developed his own theory of TT : TT is the particular language that teachers employ while addressing L2 learners in the classroom. Studies of TT can be categorized into those that look into the language that teachers use in language classrooms and those that look into the language that teachers use in topic courses. There is systematic simplification of the formal features of the instructor’s language. The language that teachers use to speak to L2 learners is viewed as a register with its own unique formal and linguistic qualities he added [15–18].The process of learning English mostly exists in classroom and the procedure that follows is an action the students take to fulfill the teaching and learning objectives. The approach used by the teachers is meant to enhance the learners’ personality while attempting to acquire new knowledge, values, and abilities. The act is characterized as the outcome of a learning process made possible by a state of physical, spiritual, social, and environmental conditions. In this instance, attitude is considered as one of the personalities that affects the learning process in addition to TT. Attitude is closely related to learning because attitudes are internal factors that can affect learning. Attitude has recently received considerable attention from both first and second language researchers. Because attitudes are internal elements that might impact learning, they are intimately tied to both learning and attitude. Both first- and second-language scholars have recently paid a lot of attention to attitude. The majority of studies on the subject have come to the conclusion that student attitudes play a crucial role in learning and should, therefore, be incorporated into second language learning methodology. Research on students’ attitudes toward language acquisition is relevant for a number of reasons. First, it is thought that attitudes toward learning affect behaviors such as choosing and reading books and speaking a foreign language [19–22]. Second, studies have demonstrated a connection between attitudes and success. The idea is that attitudes affect performance rather than performance affecting attitudes. Language teachers and scholars are becoming more and more interested in how attitudes toward learning are developed and how learning affects people. The explanation is that attitudes affect behaviors, internal moods, and ultimate learning. Therefore, it is obvious that language learning and the environment in which the learner was raised interact. Positive and negative attitudes both have a significant impact on how well language learning goes.An essential term in social psychology is attitude, which is unique and helps people to recognize their traits [23–25]. A person’s attitude is their tendency to react favorably or negatively to a particular thing, person, institution, or event [26–29]. One of the most crucial elements for success in language learning is attitude. Parents, teachers, the learning environment and classroom assignment, the reference group, one’s own experiences, and the media are some of the elements that might influence one’s attitude toward learning. Three crucial elements make up attitudes: affective, cognitive, and behavioral. The affective component, also known as the emotional aspect of attitude, includes several indicators, such as students’ acceptance of the learning process, their reactions as they learn English, their appreciation of the process, their organization of a reliable routine, and their use of the language mental component. The language learners’ perceptions of the information they learn and the concepts they comprehend during the learning process are included in the cognitive component. The behavioral component of attitude has to do with how a person acts or behaves in specific circumstances [30–32].Kara [33] stated that attitudes toward learning besides opinions and beliefs have an obvious influence on students’ behaviors and consequently on their performance. This means that students who have positive attitude toward language learning have tendency to obtain satisfied language performance. Learners’ attitude toward learning language plays an important role in motivating them to learn that language. There are many reasons why students’ attitude toward language learning is important. For instance, Weinburgh believed that attitudes toward learning influence behaviors such as selecting and reading books, speaking in a foreign language [22, 34, 35]. Generally, it is believed that learners’ attitudes dictate whether or not they will be able to absorb the details of language. In other words, achievement in a target language relies not only on intellectual capacity but also on the learner’s attitudes toward language learning [36–39].In this research, it is the oral form of TT instead of written form that is under this investigation. It refers to the correlation of TT and students’ attitude in language classrooms rather than in other settings. Two research questions are formulated in reference to this study. First of all, what types of attitude possessed by the learners and how they are classified? The second question is, how does TT correlate with the learners’ attitude in the attempt of achieving the learning goals?


### 1.1. Objectives of the Study

Based on the research questions posed above, this study aims at first, disclosing the types of attitude that are mostly demonstrated by the learners in classroom and how these attitudes are classified, and second, to find out the significant correlation of TT with the learners’ attitude when specific learning objectives are to be achieved.


### 1.2. Significance of the Study

This study is expected to be beneficial for language teachers and practitioners who are concerned with the smooth process of language teaching and learning in classrooms. The main role of TT is to smoothen the communication between teachers and learners and this is ultimate in the attempt to achieve the learning objectives. On the other side, the attitude of the learners is also crucial as it determines the success in language learning. The result of this study is even more important for future researches as reference for similar topics.


## 1.1. Objectives of the Study

Based on the research questions posed above, this study aims at first, disclosing the types of attitude that are mostly demonstrated by the learners in classroom and how these attitudes are classified, and second, to find out the significant correlation of TT with the learners’ attitude when specific learning objectives are to be achieved.


## 1.2. Significance of the Study

This study is expected to be beneficial for language teachers and practitioners who are concerned with the smooth process of language teaching and learning in classrooms. The main role of TT is to smoothen the communication between teachers and learners and this is ultimate in the attempt to achieve the learning objectives. On the other side, the attitude of the learners is also crucial as it determines the success in language learning. The result of this study is even more important for future researches as reference for similar topics.


## 2. Review of Related Literature

### 2.1. Concept of Teachers’ Talk

Some experts have given definitions about TT from a variety of perspectives. According to Longman Dictionary of Language Teaching and Applied Linguistics, TT is a type of language that teachers occasionally use while instructing students. Johnson [40] asserted that there is a tendency for the teachers to influence the pattern of communication in the context of classroom conversation. Their unique position and the way they use language give them this power. They typically decide how, when, where, and with whom language is to be used in the classroom. When assigning speaking time to students, for instance, teachers can limit students’ speaking in the classroom by either specifying the task or giving them time limit [41–43].Additionally, teachers’ special language used when conversing with L2 learners in the classroom is known as TT. The formal aspects of the teacher’s language are systematically simplified. Studies on TT can be categorized as either looking at the language instructors use in language courses or looking into the language, they use in topic lessons. Teachers see the language they use to speak to L2 learners as a register with its own unique formal and linguistic characteristics [15, 44, 45]. As a result, TT is the language that teachers use to communicate with pupils in the classroom. Talking is one of the most exceptional aspects of teacher interaction. In relation to TT, Rasyid states that talking is one of the most outstanding behaviors revealed by teachers in the classroom, which may become the most difficult thing for teachers to avoid [46–49].TT is a key tool that the teacher uses to inform, discuss, and negotiate with his students, as well as to inspire them, so he can impart knowledge and manage their conduct. Activities, such as requiring, explaining, and evaluating, are frequently dominated by teacher discourse, which restrict the amount and significance of student talk. Children consequently spend more time listening to teachers speak than they do actively interact in language with them or other students [50–52]. Students can benefit from teacher speak because the teacher uses talking to convey the material to the class. Cook [53] believes that teacher’s discussion is crucial to the teaching of languages. This means that interaction in the classroom comes through instructor speaking. Teacher speaks, according to Yanfeng and Yuqin [54], are the language used in the classroom to provide guidance, describe tasks, and assess students’ knowledge. In addition, teacher discourse is a language component of classroom conversation and engagement [55–58]. The language that kids use to communicate with their peers or the teacher in a classroom setting is referred to as students’ talk [59].This is described in all types of classrooms by Nunan [60]. The importance of teacher speaking has been thoroughly investigated and verified. It is particularly crucial in language classes because the message as the medium. The language changes that teachers make, the questions they pose, the comments they offer, and the kinds of instructions and justifications they give can all have a significant impact on the learners’ acquisition of the target language, as well as the efficient organization of the classroom. Nunan [60] indicated that we need to consider a number of variables when assessing whether or not the amount of teacher discourse is suitable, including: (1) the moment in the lesson at which the talking happens, (2) what prompts the teacher talk: whether it is spontaneous or planned, and (3) the value of the talk as potentially useful input for acquisition.Flanders’ interaction analysis developed by Flanders [61] is an instrument that has so far been used to describe the TT. It suggested the coding categories of interaction analysis to know the quantity of verbal interaction in the classroom. This is one of important techniques that can be used to observe classroom interaction systematically. The Flanders Interaction Analysis Category System (FIACS) records what teachers and learners say during the teaching and learning process. Besides that, the technique allows the teachers to see exactly what kind of verbal interaction that they use and what kind of response is given by the learners.FIACS provides three major categories to classify classroom verbal interaction. These include TT, learners talk, and silence or confusion. Each classroom verbal interaction will be coded at the end of three second’s period. This means that at three seconds interval, the observer will decide which best category of teacher and learners talk represents the completed communication. These categories will be put into columns of observational sheet to preserve the original sequence of events after the researcher do the plotting of the coded data first. Flanders’ interaction analysis is for identifying, classifying, and observing classroom verbal interaction. This means that Flanders’ interaction analysis helps the researcher to identify classroom interaction during teaching and learning process by classifying the interaction into the TT, learners talk, and silence. The following information describes the seven categories of interaction analysis and related concepts. This information represents a sampling of the type of TT. In line with these, Flanders divides the category of TT into two broad categories: “indirect influence” and “direct influence.” The first category is divided into four subcategories: (1) deals with feelings, (2) praises or encourages, (3) uses of learner’s idea, and (4) asks questions. Meanwhile, the second one is divided into three categories: (1) gives information, (2) gives directions, and (3) criticizes learners behavior. In terms of foreign language teaching, the researcher uses the FIACS analysis system to describe, analyze, and interpret the function of languages used by the teacher obtained from the classroom observations data.


### 2.2. Concept of Attitude

Attitudes are relatively sedentary tendency to react in a way that is good or bad about something. In principle, the attitude is the tendency of learners to act in a certain way. In this case, the learner learning behavior is characterized by the emergence of a new trend that has been changed to an object, values, events, and others.Attitudes and motivation are two elements that greatly influence the outcome of any education. This is due to the widespread belief that learners’ attitudes can predict their motivation. In this study, the single factor of attitudes is the main emphasis. Attitude is an organization of motivation, emotion, perception, or monitoring in a personal life element [62]. Attitude is one of the psychological elements that affects learning, including success in learning English. In general, attitude can be defined as a learner’s reaction to the target language, whether it can be good or negative.A situational understanding is necessary for attitude. The crucial part of an attitude, however, is found in the fact that certain feelings or emotions are experienced, and as we would expect from experts, a certain predisposition to act is related [63]. The emotional component is crucial in determining attitude. Reaction, response, or a propensity to react is the second factor. In this situation, attitude is a key factor in determining human behavior.Attitude is a concept that helps in understanding human behavior. Travers, Gagne, and Cronbach in Ahmadi [62] agreed that attitude involves three components that interact with the object. These components include:(1)
Cognitive component is associated with knowledge, beliefs, or thoughts that are based on information associated with the object. For example, people know that it is worth the money because they see the price in daily life. Our attitude toward money implies that we know about the value of money(2)
Affective component refers to the emotional dimension of attitudes and emotions that are associated with the object. Here, the object is perceived as pleasant or unpleasant. For example, if someone says that they are happy with the money, these describe their feelings toward money(3)
Behavior or conative component involves one of predisposition to act toward the object. For example, because the money is worth something, people liked it and they are trying to get a big salary. Choyimah gives addition that behavioral aspect of attitude deals with how learners react and behave in particular condition. In the context of English teaching and learning, behavioral aspect of attitude can be seen from how learners react to native speakers’ way in speaking English. The learners’ eagerness in imitating native speakers’ ways in speaking English, and their desire to learn or not to learn more about the culture of English-speaking countries are just a few examples of behavioral aspect of attitudeFrom the discussion elicited above, this particular study is concerned with how TT interplays with learners’ attitude in which the three components of human behavior are taken into account. This research novelty will, in turn, inform the pedagogical practices of language teachers and practitioners.


## 2.1. Concept of Teachers’ Talk

Some experts have given definitions about TT from a variety of perspectives. According to Longman Dictionary of Language Teaching and Applied Linguistics, TT is a type of language that teachers occasionally use while instructing students. Johnson [40] asserted that there is a tendency for the teachers to influence the pattern of communication in the context of classroom conversation. Their unique position and the way they use language give them this power. They typically decide how, when, where, and with whom language is to be used in the classroom. When assigning speaking time to students, for instance, teachers can limit students’ speaking in the classroom by either specifying the task or giving them time limit [41–43].Additionally, teachers’ special language used when conversing with L2 learners in the classroom is known as TT. The formal aspects of the teacher’s language are systematically simplified. Studies on TT can be categorized as either looking at the language instructors use in language courses or looking into the language, they use in topic lessons. Teachers see the language they use to speak to L2 learners as a register with its own unique formal and linguistic characteristics [15, 44, 45]. As a result, TT is the language that teachers use to communicate with pupils in the classroom. Talking is one of the most exceptional aspects of teacher interaction. In relation to TT, Rasyid states that talking is one of the most outstanding behaviors revealed by teachers in the classroom, which may become the most difficult thing for teachers to avoid [46–49].TT is a key tool that the teacher uses to inform, discuss, and negotiate with his students, as well as to inspire them, so he can impart knowledge and manage their conduct. Activities, such as requiring, explaining, and evaluating, are frequently dominated by teacher discourse, which restrict the amount and significance of student talk. Children consequently spend more time listening to teachers speak than they do actively interact in language with them or other students [50–52]. Students can benefit from teacher speak because the teacher uses talking to convey the material to the class. Cook [53] believes that teacher’s discussion is crucial to the teaching of languages. This means that interaction in the classroom comes through instructor speaking. Teacher speaks, according to Yanfeng and Yuqin [54], are the language used in the classroom to provide guidance, describe tasks, and assess students’ knowledge. In addition, teacher discourse is a language component of classroom conversation and engagement [55–58]. The language that kids use to communicate with their peers or the teacher in a classroom setting is referred to as students’ talk [59].This is described in all types of classrooms by Nunan [60]. The importance of teacher speaking has been thoroughly investigated and verified. It is particularly crucial in language classes because the message as the medium. The language changes that teachers make, the questions they pose, the comments they offer, and the kinds of instructions and justifications they give can all have a significant impact on the learners’ acquisition of the target language, as well as the efficient organization of the classroom. Nunan [60] indicated that we need to consider a number of variables when assessing whether or not the amount of teacher discourse is suitable, including: (1) the moment in the lesson at which the talking happens, (2) what prompts the teacher talk: whether it is spontaneous or planned, and (3) the value of the talk as potentially useful input for acquisition.Flanders’ interaction analysis developed by Flanders [61] is an instrument that has so far been used to describe the TT. It suggested the coding categories of interaction analysis to know the quantity of verbal interaction in the classroom. This is one of important techniques that can be used to observe classroom interaction systematically. The Flanders Interaction Analysis Category System (FIACS) records what teachers and learners say during the teaching and learning process. Besides that, the technique allows the teachers to see exactly what kind of verbal interaction that they use and what kind of response is given by the learners.FIACS provides three major categories to classify classroom verbal interaction. These include TT, learners talk, and silence or confusion. Each classroom verbal interaction will be coded at the end of three second’s period. This means that at three seconds interval, the observer will decide which best category of teacher and learners talk represents the completed communication. These categories will be put into columns of observational sheet to preserve the original sequence of events after the researcher do the plotting of the coded data first. Flanders’ interaction analysis is for identifying, classifying, and observing classroom verbal interaction. This means that Flanders’ interaction analysis helps the researcher to identify classroom interaction during teaching and learning process by classifying the interaction into the TT, learners talk, and silence. The following information describes the seven categories of interaction analysis and related concepts. This information represents a sampling of the type of TT. In line with these, Flanders divides the category of TT into two broad categories: “indirect influence” and “direct influence.” The first category is divided into four subcategories: (1) deals with feelings, (2) praises or encourages, (3) uses of learner’s idea, and (4) asks questions. Meanwhile, the second one is divided into three categories: (1) gives information, (2) gives directions, and (3) criticizes learners behavior. In terms of foreign language teaching, the researcher uses the FIACS analysis system to describe, analyze, and interpret the function of languages used by the teacher obtained from the classroom observations data.


## 2.2. Concept of Attitude

Attitudes are relatively sedentary tendency to react in a way that is good or bad about something. In principle, the attitude is the tendency of learners to act in a certain way. In this case, the learner learning behavior is characterized by the emergence of a new trend that has been changed to an object, values, events, and others.Attitudes and motivation are two elements that greatly influence the outcome of any education. This is due to the widespread belief that learners’ attitudes can predict their motivation. In this study, the single factor of attitudes is the main emphasis. Attitude is an organization of motivation, emotion, perception, or monitoring in a personal life element [62]. Attitude is one of the psychological elements that affects learning, including success in learning English. In general, attitude can be defined as a learner’s reaction to the target language, whether it can be good or negative.A situational understanding is necessary for attitude. The crucial part of an attitude, however, is found in the fact that certain feelings or emotions are experienced, and as we would expect from experts, a certain predisposition to act is related [63]. The emotional component is crucial in determining attitude. Reaction, response, or a propensity to react is the second factor. In this situation, attitude is a key factor in determining human behavior.Attitude is a concept that helps in understanding human behavior. Travers, Gagne, and Cronbach in Ahmadi [62] agreed that attitude involves three components that interact with the object. These components include:(1)
Cognitive component is associated with knowledge, beliefs, or thoughts that are based on information associated with the object. For example, people know that it is worth the money because they see the price in daily life. Our attitude toward money implies that we know about the value of money(2)
Affective component refers to the emotional dimension of attitudes and emotions that are associated with the object. Here, the object is perceived as pleasant or unpleasant. For example, if someone says that they are happy with the money, these describe their feelings toward money(3)
Behavior or conative component involves one of predisposition to act toward the object. For example, because the money is worth something, people liked it and they are trying to get a big salary. Choyimah gives addition that behavioral aspect of attitude deals with how learners react and behave in particular condition. In the context of English teaching and learning, behavioral aspect of attitude can be seen from how learners react to native speakers’ way in speaking English. The learners’ eagerness in imitating native speakers’ ways in speaking English, and their desire to learn or not to learn more about the culture of English-speaking countries are just a few examples of behavioral aspect of attitudeFrom the discussion elicited above, this particular study is concerned with how TT interplays with learners’ attitude in which the three components of human behavior are taken into account. This research novelty will, in turn, inform the pedagogical practices of language teachers and practitioners.


## 3. Research Method

### 3.1. Design of the Study

This study is quantitative in nature utilizing correlational method. Indexes produced by correlational research demonstrate the direction and magnitude of correlations between variables while accounting for the entire range of these variables. This paradigm looks closely at TT as the integral part of classroom instruction and its correlation with the learners’ classroom attitude comprising three classification systems [64].


### 3.2. Participants

Sixth-grade learners from two high schools in Sidrap, namely, SMAN 6 and SMKs Harapan Bangsa in 2021–2020 academic year were taken as respondents. One hundred sixty learners were then taken as samples, selected through random sampling technique.


### 3.3. Instruments

In order to determine whether TT and students’ attitudes are related, the researchers utilized questionnaires as research instrument. The data from the questionnaire were evaluated for validity, reliability, regression, and correlation using the alternative options of strongly agree, agree, neutral, disagree, and strongly disagree. This data collection was conducted throughout the even semester of the endemic era.


### 3.4. Data Analysis

The gathered data were analyzed statistically using SPSS 24.0. This involved finding and compiling information from test results and the responses to the questionnaire, as well as learning how to reduce data, i.e., focus attention, simplify, abstract, and transform raw data in the field [65].


## 3.1. Design of the Study

This study is quantitative in nature utilizing correlational method. Indexes produced by correlational research demonstrate the direction and magnitude of correlations between variables while accounting for the entire range of these variables. This paradigm looks closely at TT as the integral part of classroom instruction and its correlation with the learners’ classroom attitude comprising three classification systems [64].


## 3.2. Participants

Sixth-grade learners from two high schools in Sidrap, namely, SMAN 6 and SMKs Harapan Bangsa in 2021–2020 academic year were taken as respondents. One hundred sixty learners were then taken as samples, selected through random sampling technique.


## 3.3. Instruments

In order to determine whether TT and students’ attitudes are related, the researchers utilized questionnaires as research instrument. The data from the questionnaire were evaluated for validity, reliability, regression, and correlation using the alternative options of strongly agree, agree, neutral, disagree, and strongly disagree. This data collection was conducted throughout the even semester of the endemic era.


## 3.4. Data Analysis

The gathered data were analyzed statistically using SPSS 24.0. This involved finding and compiling information from test results and the responses to the questionnaire, as well as learning how to reduce data, i.e., focus attention, simplify, abstract, and transform raw data in the field [65].


## 4. Results

### 4.1. Students’ Attitude in Learning English

In this study, questionnaire was used to get the data about the learners’ attitude toward cognitive component, affective component, and behavioral or conative component. The data obtained from the learners’ attitude scores of questionnaires consist of statements about learners’ attitude toward English. Total items are 10 questions. The lowest possible score is 28 and the highest score is 50. The learners’ score of attitudes toward English was obtained from the results of distributing questionnaires to the learners. The following is the result of learners’ scores obtained from answering the questionnaires.The criteria of giving the learners’ scores were based on data analysis. If the scores of learners’ attitude are ≥3, it means that the criteria of learners’ attitude are positive, but if scores of learners’ attitude are <3, the criteria of learners’ attitude are negative.Based on the criteria of learners’ attitude, the score ≥3 indicates positive level of the learners’ attitude. This study shows that 159 out of 160 learners had positive attitude. This means that almost all of the learners have positive attitudes. In other words, there are 99.38% of learners had positive attitudes in learning English. The following is the classification of learners’ attitude scores (Figure1).Figure 1
Learners’ attitude scores.Based on the data stated in Table1, it can be identified that the most preferred attitude of the respondents is cognitive and all learners have positive attitude in learning English. The classification of learners’ behavioral, cognitive, and emotional attitude is as follows (Figure 2 and Table 2).Figure 2
Learners’ attitude classification.Table 1
Classification of learners’ attitude scores.
ScoreFrequencyClassification<31Negative≥3159PositiveTable 2
Learners’ attitudes dimensions.
AttitudeMean scoreClassificationBehavioral4.19PositiveCognitive4.22PositiveEmotional4.02Positive


### 4.2. Hypothesis Testing

#### 4.2.1. Normality Test

This test is intended to determine whether the existing data are normally distributed or not. Data that are normally distributed will minimize the likelihood of bias occurrence. A good regression model has normally distributed data. The normality test of the data can be detected by looking at the histogram chart and Kolmogorov–Smirnov test with the help of SPSS. The result of the normality test with histogram graph and SPSS plot and Kolmogorov–Smirnov test from normality test of this research is shown in Figure3.Figure 3
Normality test of teachers’ talk and attitude.The histogram and plot may be seen in the graph output of normality test of TT and learning style and attitude. It shows a distribution pattern that deviates to the right, indicating that the data are normally distributed. The dots can also be seen approaching and following their diagonals in the P-plot image, indicating that the regression model satisfies the normality condition.


#### 4.2.2. Linearity Test

Linearity test is intended to determine the relationship pattern of the independent variable and the dependent variable whether it is linear or not. The linearity test can be determined using theF-test; the data are processed using the help of the SPSS version 23.0 program by looking at the significance of the deviation from linearity of the linear F-test or linear scatter-plot graph. The complete calculation is attached as follows and is presented through the linearity test histograms in Figure 4.Figure 4
Linearity teachers’ talk to learning attitude.


#### 4.2.3. Validity Test

Validity test is used to measure the accuracy of the questionnaire. A questionnaire is considered valid if the questions in the questionnaire reflect the overall content being tested. Thus, the researcher must analyze whether the questions represent the entire content or not in order to ensure that the questionnaire is valid. Addressing validity concerns the nature of what validity means, and how to know if one has achieved an acceptable level of validity enters design, inferences, and conclusions [66].Validity test was performed by comparing the value ofr-count and r-table. In this research, the r-table is 0.1552 because the degree of freedom is 160 and the value of alpha is 0.05. If r-count ≥ r-table and have positive value, it can be concluded that the questionnaire is valid. On the contrary, if r-count < r-table, it means that the questionnaire is invalid (Table 3).Table 3
Validity test results.
VariablesQuestionr-Tabler-CountExplanationTeachers’ talkS10.15520.476ValidS20.15520.478ValidS30.15520.526ValidS40.15520.484ValidS50.15520.315ValidS60.15520.386ValidS70.15520.601ValidS80.15520.601ValidS90.15520.709ValidS100.15520.683ValidTeachers’ talkS110.15520.476ValidS120.15520.483ValidS130.15520.722ValidS140.15520.624ValidS150.15520.622ValidS160.15520.617ValidS170.15520.431ValidS180.15520.438ValidS190.15520.545ValidS200.15520.352ValidS460.15520.722Valid


#### 4.2.4. Reliability Test

Reliability test is used to measure the extent to which the results of a test can be trusted. The results can be trusted if in several times of the measurement on the same subject, the results are relatively constant, with the condition that the aspects measured on the subject have not changed. The number of Cronbach’s alpha is used to estimate whether the response of respondents is reliable or not. If the scores obtained are consistent and the number of Cronbach’s alpha >0.70, the response of respondents is considered reliable. Otherwise, if the number of Cronbach’s alpha ≤0.70, it means that the response of respondents is not reliable. The result of reliability test is presented in Table4.Table 4
Reliability test results.
VariableCronbach’s alphaExplanationTeachers’ talk0.733ReliableAttitude0.729ReliableBased on Table4, it is shown that the number of Cronbach’s alpha in variable TT is 0.733 and attitude is 0.729. All variables have Cronbach’s alpha >0.70; thus, all the variables are reliable.


#### 4.2.5. Simple Linear Regressions of Teachers’ Talk and Attitude

TheR-value illustrates the relationship between the dependent and independent variables (Table 5). A number is taken that is >0.3 for further analysis. In this case, the value is 0.317, which is a good result. The overall variation for the dependent variable that the independent variables might account for is shown by the R-square. A value >0.9 suggests that the model can recognize the relationship. In this case, the value is 0.095, which is a good result. The adjusted R-square in multiple regression illustrates the generalizability of the results or the divergence of the sample results from the population. There must be at least a small discrepancy between R-square and adjusted R-square. The value in this case is 0.095, which is acceptable because it is close to the threshold of 0.317 (Table 5).Table 5
Coefficient of teachers’ talk to attitude.
ModelRR-squareAdjustedR-squareStandard error of the estimate10.3170.1010.0953.622The components in Table6 are important for understanding the findings: p-value/Sig. value, first: in most cases, the study’s significance level is set at 5% or the 95% confidence interval. Consequently, the p-value must be <0.05. It is 0.001 in Table 6. Consequently, the outcome is crucial; F-ratio, second: after taking into account the model’s inherent inaccuracy, it represents an improvement in the variable’s predicted. A value for the F-ratio yield efficient model is >1. The value in Table 6 is 17.704, which is favorable. These findings suggest that the null hypothesis may be rejected further because the p-value of the ANOVA table is below the acceptable level of significance. The table displays the relationship’s strength or how significant a variable is to the model and how much it influences the dependent variable. This analysis supports the study’s hypothesis testing.Table 6
Significance of teachers’ talk to attitude.
ModelSum of squaresdfMean squareFSig.1Regression232.1881232.18817.7040.001Residual2072.21215813.115Total2304.400159The only value that matters during the interpretation process is the signature value. The value must be below the acceptable level of significance for the study, which for this study is <0.05 for the 95% confidence interval. The significant value establishes the viability of the null hypothesis. If Sig. value is not higher than 0.05, the null hypothesis is not disproved. If Sig. >0.05, the null hypothesis is not disproved. It is clear that there is an impact when the null hypothesis is rejected. A null hypothesis, however, indicates that no effect exists if it is not refuted. This will be interpreted as the null hypothesis being rejected (0.001 > 0.05). There has not been much of a shift in how TT about attitude. This is because the signature value is higher (Table7).Table 7
Coefficient regression of teachers’ talk to attitude.
ModelUnstandardized coefficientsStandardized coefficientstSig.BStandard errorβ1(Constant)31.2192.49512.5100.001Teacher_talk0.2680.0640.3174.2080.001A measure of the strength of a linear regression between two variables, the Pearson product-moment correlation coefficient (or Pearson correlation coefficient, for short), is represented by the symbolr. The Pearson correlation coefficient, r, shows how far all of these data points are from this line of best fit (i.e., how well the data points fit this new model/line of best fit). Basically, a Pearson product-moment correlation attempts to draw a line of best fit through the data of two variables (Table 8).Table 8
Correlation of teachers’ talk to attitude.
Teacher_talkAttitudeTeacher_talkPearson correlation10.350Sig. (two-tailed)0.001N160160AttitudePearson correlation0.3501Sig. (two-tailed)0.001N160160The next step is the correlation coefficient or Pearson’sr-value. The Pearson correlation value, which in this case is 0.350 and is shown above inside the square red box in Table 8, shows a tenuous positive correlation.


#### 4.2.6.t-Test

t-tests are used to compare the mean scores of two groups of people or conditions. There are two types of t-tests: independent-samples t-test, which compares the mean scores of two different groups of people or conditions, and paired-samples t-test, which compares the mean scores for the same group of people on two different occasions. Statistical significance is often referred to as the p-value (short for “probability value”) or simply p in research papers. A small p-value basically means that your data are unlikely under some null hypothesis. The result of the learners’ mean score the interplay of TT and attitude as part of individual differences of secondary high school of Watang Pulu, Sidrap, was analyzed by using independent-samples t-test, and presented in Table 9.Table 9
The analysis oft-test of teachers’ talk to attitude.
ModelUnstandardized coefficientsStandardized coefficientstSig.BStandard errorβ1(Constant)31.2192.49512.5100.001Teacher_talk0.2680.0640.3174.2080.001Thet-test result was 12.510 and df was 160. In conclusion, p-value was 0.001, which two-tailed value was <0.05 (alpha value). The result showed that the scores differ much between significance and 0.05. The researcher concluded that there is a significant interplay between TT and English language learners’ attitude in secondary high school.


## 4.1. Students’ Attitude in Learning English

In this study, questionnaire was used to get the data about the learners’ attitude toward cognitive component, affective component, and behavioral or conative component. The data obtained from the learners’ attitude scores of questionnaires consist of statements about learners’ attitude toward English. Total items are 10 questions. The lowest possible score is 28 and the highest score is 50. The learners’ score of attitudes toward English was obtained from the results of distributing questionnaires to the learners. The following is the result of learners’ scores obtained from answering the questionnaires.The criteria of giving the learners’ scores were based on data analysis. If the scores of learners’ attitude are ≥3, it means that the criteria of learners’ attitude are positive, but if scores of learners’ attitude are <3, the criteria of learners’ attitude are negative.Based on the criteria of learners’ attitude, the score ≥3 indicates positive level of the learners’ attitude. This study shows that 159 out of 160 learners had positive attitude. This means that almost all of the learners have positive attitudes. In other words, there are 99.38% of learners had positive attitudes in learning English. The following is the classification of learners’ attitude scores (Figure1).Figure 1
Learners’ attitude scores.Based on the data stated in Table1, it can be identified that the most preferred attitude of the respondents is cognitive and all learners have positive attitude in learning English. The classification of learners’ behavioral, cognitive, and emotional attitude is as follows (Figure 2 and Table 2).Figure 2
Learners’ attitude classification.Table 1
Classification of learners’ attitude scores.
ScoreFrequencyClassification<31Negative≥3159PositiveTable 2
Learners’ attitudes dimensions.
AttitudeMean scoreClassificationBehavioral4.19PositiveCognitive4.22PositiveEmotional4.02Positive


## 4.2. Hypothesis Testing

### 4.2.1. Normality Test

This test is intended to determine whether the existing data are normally distributed or not. Data that are normally distributed will minimize the likelihood of bias occurrence. A good regression model has normally distributed data. The normality test of the data can be detected by looking at the histogram chart and Kolmogorov–Smirnov test with the help of SPSS. The result of the normality test with histogram graph and SPSS plot and Kolmogorov–Smirnov test from normality test of this research is shown in Figure3.Figure 3
Normality test of teachers’ talk and attitude.The histogram and plot may be seen in the graph output of normality test of TT and learning style and attitude. It shows a distribution pattern that deviates to the right, indicating that the data are normally distributed. The dots can also be seen approaching and following their diagonals in the P-plot image, indicating that the regression model satisfies the normality condition.


### 4.2.2. Linearity Test

Linearity test is intended to determine the relationship pattern of the independent variable and the dependent variable whether it is linear or not. The linearity test can be determined using theF-test; the data are processed using the help of the SPSS version 23.0 program by looking at the significance of the deviation from linearity of the linear F-test or linear scatter-plot graph. The complete calculation is attached as follows and is presented through the linearity test histograms in Figure 4.Figure 4
Linearity teachers’ talk to learning attitude.


### 4.2.3. Validity Test

Validity test is used to measure the accuracy of the questionnaire. A questionnaire is considered valid if the questions in the questionnaire reflect the overall content being tested. Thus, the researcher must analyze whether the questions represent the entire content or not in order to ensure that the questionnaire is valid. Addressing validity concerns the nature of what validity means, and how to know if one has achieved an acceptable level of validity enters design, inferences, and conclusions [66].Validity test was performed by comparing the value ofr-count and r-table. In this research, the r-table is 0.1552 because the degree of freedom is 160 and the value of alpha is 0.05. If r-count ≥ r-table and have positive value, it can be concluded that the questionnaire is valid. On the contrary, if r-count < r-table, it means that the questionnaire is invalid (Table 3).Table 3
Validity test results.
VariablesQuestionr-Tabler-CountExplanationTeachers’ talkS10.15520.476ValidS20.15520.478ValidS30.15520.526ValidS40.15520.484ValidS50.15520.315ValidS60.15520.386ValidS70.15520.601ValidS80.15520.601ValidS90.15520.709ValidS100.15520.683ValidTeachers’ talkS110.15520.476ValidS120.15520.483ValidS130.15520.722ValidS140.15520.624ValidS150.15520.622ValidS160.15520.617ValidS170.15520.431ValidS180.15520.438ValidS190.15520.545ValidS200.15520.352ValidS460.15520.722Valid


### 4.2.4. Reliability Test

Reliability test is used to measure the extent to which the results of a test can be trusted. The results can be trusted if in several times of the measurement on the same subject, the results are relatively constant, with the condition that the aspects measured on the subject have not changed. The number of Cronbach’s alpha is used to estimate whether the response of respondents is reliable or not. If the scores obtained are consistent and the number of Cronbach’s alpha >0.70, the response of respondents is considered reliable. Otherwise, if the number of Cronbach’s alpha ≤0.70, it means that the response of respondents is not reliable. The result of reliability test is presented in Table4.Table 4
Reliability test results.
VariableCronbach’s alphaExplanationTeachers’ talk0.733ReliableAttitude0.729ReliableBased on Table4, it is shown that the number of Cronbach’s alpha in variable TT is 0.733 and attitude is 0.729. All variables have Cronbach’s alpha >0.70; thus, all the variables are reliable.


### 4.2.5. Simple Linear Regressions of Teachers’ Talk and Attitude

TheR-value illustrates the relationship between the dependent and independent variables (Table 5). A number is taken that is >0.3 for further analysis. In this case, the value is 0.317, which is a good result. The overall variation for the dependent variable that the independent variables might account for is shown by the R-square. A value >0.9 suggests that the model can recognize the relationship. In this case, the value is 0.095, which is a good result. The adjusted R-square in multiple regression illustrates the generalizability of the results or the divergence of the sample results from the population. There must be at least a small discrepancy between R-square and adjusted R-square. The value in this case is 0.095, which is acceptable because it is close to the threshold of 0.317 (Table 5).Table 5
Coefficient of teachers’ talk to attitude.
ModelRR-squareAdjustedR-squareStandard error of the estimate10.3170.1010.0953.622The components in Table6 are important for understanding the findings: p-value/Sig. value, first: in most cases, the study’s significance level is set at 5% or the 95% confidence interval. Consequently, the p-value must be <0.05. It is 0.001 in Table 6. Consequently, the outcome is crucial; F-ratio, second: after taking into account the model’s inherent inaccuracy, it represents an improvement in the variable’s predicted. A value for the F-ratio yield efficient model is >1. The value in Table 6 is 17.704, which is favorable. These findings suggest that the null hypothesis may be rejected further because the p-value of the ANOVA table is below the acceptable level of significance. The table displays the relationship’s strength or how significant a variable is to the model and how much it influences the dependent variable. This analysis supports the study’s hypothesis testing.Table 6
Significance of teachers’ talk to attitude.
ModelSum of squaresdfMean squareFSig.1Regression232.1881232.18817.7040.001Residual2072.21215813.115Total2304.400159The only value that matters during the interpretation process is the signature value. The value must be below the acceptable level of significance for the study, which for this study is <0.05 for the 95% confidence interval. The significant value establishes the viability of the null hypothesis. If Sig. value is not higher than 0.05, the null hypothesis is not disproved. If Sig. >0.05, the null hypothesis is not disproved. It is clear that there is an impact when the null hypothesis is rejected. A null hypothesis, however, indicates that no effect exists if it is not refuted. This will be interpreted as the null hypothesis being rejected (0.001 > 0.05). There has not been much of a shift in how TT about attitude. This is because the signature value is higher (Table7).Table 7
Coefficient regression of teachers’ talk to attitude.
ModelUnstandardized coefficientsStandardized coefficientstSig.BStandard errorβ1(Constant)31.2192.49512.5100.001Teacher_talk0.2680.0640.3174.2080.001A measure of the strength of a linear regression between two variables, the Pearson product-moment correlation coefficient (or Pearson correlation coefficient, for short), is represented by the symbolr. The Pearson correlation coefficient, r, shows how far all of these data points are from this line of best fit (i.e., how well the data points fit this new model/line of best fit). Basically, a Pearson product-moment correlation attempts to draw a line of best fit through the data of two variables (Table 8).Table 8
Correlation of teachers’ talk to attitude.
Teacher_talkAttitudeTeacher_talkPearson correlation10.350Sig. (two-tailed)0.001N160160AttitudePearson correlation0.3501Sig. (two-tailed)0.001N160160The next step is the correlation coefficient or Pearson’sr-value. The Pearson correlation value, which in this case is 0.350 and is shown above inside the square red box in Table 8, shows a tenuous positive correlation.


### 4.2.6.t-Test

t-tests are used to compare the mean scores of two groups of people or conditions. There are two types of t-tests: independent-samples t-test, which compares the mean scores of two different groups of people or conditions, and paired-samples t-test, which compares the mean scores for the same group of people on two different occasions. Statistical significance is often referred to as the p-value (short for “probability value”) or simply p in research papers. A small p-value basically means that your data are unlikely under some null hypothesis. The result of the learners’ mean score the interplay of TT and attitude as part of individual differences of secondary high school of Watang Pulu, Sidrap, was analyzed by using independent-samples t-test, and presented in Table 9.Table 9
The analysis oft-test of teachers’ talk to attitude.
ModelUnstandardized coefficientsStandardized coefficientstSig.BStandard errorβ1(Constant)31.2192.49512.5100.001Teacher_talk0.2680.0640.3174.2080.001Thet-test result was 12.510 and df was 160. In conclusion, p-value was 0.001, which two-tailed value was <0.05 (alpha value). The result showed that the scores differ much between significance and 0.05. The researcher concluded that there is a significant interplay between TT and English language learners’ attitude in secondary high school.


## 4.2.1. Normality Test

This test is intended to determine whether the existing data are normally distributed or not. Data that are normally distributed will minimize the likelihood of bias occurrence. A good regression model has normally distributed data. The normality test of the data can be detected by looking at the histogram chart and Kolmogorov–Smirnov test with the help of SPSS. The result of the normality test with histogram graph and SPSS plot and Kolmogorov–Smirnov test from normality test of this research is shown in Figure3.Figure 3
Normality test of teachers’ talk and attitude.The histogram and plot may be seen in the graph output of normality test of TT and learning style and attitude. It shows a distribution pattern that deviates to the right, indicating that the data are normally distributed. The dots can also be seen approaching and following their diagonals in the P-plot image, indicating that the regression model satisfies the normality condition.


## 4.2.2. Linearity Test

Linearity test is intended to determine the relationship pattern of the independent variable and the dependent variable whether it is linear or not. The linearity test can be determined using theF-test; the data are processed using the help of the SPSS version 23.0 program by looking at the significance of the deviation from linearity of the linear F-test or linear scatter-plot graph. The complete calculation is attached as follows and is presented through the linearity test histograms in Figure 4.Figure 4
Linearity teachers’ talk to learning attitude.


## 4.2.3. Validity Test

Validity test is used to measure the accuracy of the questionnaire. A questionnaire is considered valid if the questions in the questionnaire reflect the overall content being tested. Thus, the researcher must analyze whether the questions represent the entire content or not in order to ensure that the questionnaire is valid. Addressing validity concerns the nature of what validity means, and how to know if one has achieved an acceptable level of validity enters design, inferences, and conclusions [66].Validity test was performed by comparing the value ofr-count and r-table. In this research, the r-table is 0.1552 because the degree of freedom is 160 and the value of alpha is 0.05. If r-count ≥ r-table and have positive value, it can be concluded that the questionnaire is valid. On the contrary, if r-count < r-table, it means that the questionnaire is invalid (Table 3).Table 3
Validity test results.
VariablesQuestionr-Tabler-CountExplanationTeachers’ talkS10.15520.476ValidS20.15520.478ValidS30.15520.526ValidS40.15520.484ValidS50.15520.315ValidS60.15520.386ValidS70.15520.601ValidS80.15520.601ValidS90.15520.709ValidS100.15520.683ValidTeachers’ talkS110.15520.476ValidS120.15520.483ValidS130.15520.722ValidS140.15520.624ValidS150.15520.622ValidS160.15520.617ValidS170.15520.431ValidS180.15520.438ValidS190.15520.545ValidS200.15520.352ValidS460.15520.722Valid


## 4.2.4. Reliability Test

Reliability test is used to measure the extent to which the results of a test can be trusted. The results can be trusted if in several times of the measurement on the same subject, the results are relatively constant, with the condition that the aspects measured on the subject have not changed. The number of Cronbach’s alpha is used to estimate whether the response of respondents is reliable or not. If the scores obtained are consistent and the number of Cronbach’s alpha >0.70, the response of respondents is considered reliable. Otherwise, if the number of Cronbach’s alpha ≤0.70, it means that the response of respondents is not reliable. The result of reliability test is presented in Table4.Table 4
Reliability test results.
VariableCronbach’s alphaExplanationTeachers’ talk0.733ReliableAttitude0.729ReliableBased on Table4, it is shown that the number of Cronbach’s alpha in variable TT is 0.733 and attitude is 0.729. All variables have Cronbach’s alpha >0.70; thus, all the variables are reliable.


## 4.2.5. Simple Linear Regressions of Teachers’ Talk and Attitude

TheR-value illustrates the relationship between the dependent and independent variables (Table 5). A number is taken that is >0.3 for further analysis. In this case, the value is 0.317, which is a good result. The overall variation for the dependent variable that the independent variables might account for is shown by the R-square. A value >0.9 suggests that the model can recognize the relationship. In this case, the value is 0.095, which is a good result. The adjusted R-square in multiple regression illustrates the generalizability of the results or the divergence of the sample results from the population. There must be at least a small discrepancy between R-square and adjusted R-square. The value in this case is 0.095, which is acceptable because it is close to the threshold of 0.317 (Table 5).Table 5
Coefficient of teachers’ talk to attitude.
ModelRR-squareAdjustedR-squareStandard error of the estimate10.3170.1010.0953.622The components in Table6 are important for understanding the findings: p-value/Sig. value, first: in most cases, the study’s significance level is set at 5% or the 95% confidence interval. Consequently, the p-value must be <0.05. It is 0.001 in Table 6. Consequently, the outcome is crucial; F-ratio, second: after taking into account the model’s inherent inaccuracy, it represents an improvement in the variable’s predicted. A value for the F-ratio yield efficient model is >1. The value in Table 6 is 17.704, which is favorable. These findings suggest that the null hypothesis may be rejected further because the p-value of the ANOVA table is below the acceptable level of significance. The table displays the relationship’s strength or how significant a variable is to the model and how much it influences the dependent variable. This analysis supports the study’s hypothesis testing.Table 6
Significance of teachers’ talk to attitude.
ModelSum of squaresdfMean squareFSig.1Regression232.1881232.18817.7040.001Residual2072.21215813.115Total2304.400159The only value that matters during the interpretation process is the signature value. The value must be below the acceptable level of significance for the study, which for this study is <0.05 for the 95% confidence interval. The significant value establishes the viability of the null hypothesis. If Sig. value is not higher than 0.05, the null hypothesis is not disproved. If Sig. >0.05, the null hypothesis is not disproved. It is clear that there is an impact when the null hypothesis is rejected. A null hypothesis, however, indicates that no effect exists if it is not refuted. This will be interpreted as the null hypothesis being rejected (0.001 > 0.05). There has not been much of a shift in how TT about attitude. This is because the signature value is higher (Table7).Table 7
Coefficient regression of teachers’ talk to attitude.
ModelUnstandardized coefficientsStandardized coefficientstSig.BStandard errorβ1(Constant)31.2192.49512.5100.001Teacher_talk0.2680.0640.3174.2080.001A measure of the strength of a linear regression between two variables, the Pearson product-moment correlation coefficient (or Pearson correlation coefficient, for short), is represented by the symbolr. The Pearson correlation coefficient, r, shows how far all of these data points are from this line of best fit (i.e., how well the data points fit this new model/line of best fit). Basically, a Pearson product-moment correlation attempts to draw a line of best fit through the data of two variables (Table 8).Table 8
Correlation of teachers’ talk to attitude.
Teacher_talkAttitudeTeacher_talkPearson correlation10.350Sig. (two-tailed)0.001N160160AttitudePearson correlation0.3501Sig. (two-tailed)0.001N160160The next step is the correlation coefficient or Pearson’sr-value. The Pearson correlation value, which in this case is 0.350 and is shown above inside the square red box in Table 8, shows a tenuous positive correlation.


## 4.2.6.t-Test

t-tests are used to compare the mean scores of two groups of people or conditions. There are two types of t-tests: independent-samples t-test, which compares the mean scores of two different groups of people or conditions, and paired-samples t-test, which compares the mean scores for the same group of people on two different occasions. Statistical significance is often referred to as the p-value (short for “probability value”) or simply p in research papers. A small p-value basically means that your data are unlikely under some null hypothesis. The result of the learners’ mean score the interplay of TT and attitude as part of individual differences of secondary high school of Watang Pulu, Sidrap, was analyzed by using independent-samples t-test, and presented in Table 9.Table 9
The analysis oft-test of teachers’ talk to attitude.
ModelUnstandardized coefficientsStandardized coefficientstSig.BStandard errorβ1(Constant)31.2192.49512.5100.001Teacher_talk0.2680.0640.3174.2080.001Thet-test result was 12.510 and df was 160. In conclusion, p-value was 0.001, which two-tailed value was <0.05 (alpha value). The result showed that the scores differ much between significance and 0.05. The researcher concluded that there is a significant interplay between TT and English language learners’ attitude in secondary high school.


## 5. Discussion

The findings presented above have clearly demonstrated the correlation existing between TT and students’ attitude, which carries three different components in relation to behavioral conditions of learners while being in classroom learning. Based on the questionnaire responses, most participants had positive attitudes toward learning English at the two schools although the language preference of students is mostly the native language, i.e., Bahasa Indonesia to be used as daily communication. Most of the participants (99.37%) had positive responses about learning English, while others had negative attitude (0.63%). Table2 shows the classification of learners’ attitude scores, i.e., cognitive attitude has positive response from the learner (4.22), behavioral attitude (4.19), and emotional attitude (4.02).The behavioral attitude showed the mean score of 4.16, which indicates positive category of attitude. Those ideas are also similar to Zacharias [67] in which it is confirmed that materials given in language classroom indirectly encourage the students to learn more about what had been studied, so that the students could improve their ability in language learning. Yet, to enhance student’s curiosity toward the materials and subsequently apply what they have learnt, first of all, teacher needs to grab students’ attention. According to the data from the questionnaire, 4.16 of behavioral statement indicates that the students were able to make themselves happy and pay attention during studying English. This is related to the theory of behavioral aspect of attitude that students’ preferences will affect on respectable actions or behavioral intentions toward the object and finally lead to positive attitude [33].The data analysis of the emotional attitude showed that the majority of participants had great desires to learn English (about 4.02). This result is related to Feng and Chen [68] who postulated that learning process is an emotional process, which influences students’ perspectives and attitudes toward the learning process. Most of the participants liked, enjoyed, even felt excited, and proud of learning anything about English. In line with this, many participants showed their positive attitude on the behavioral aspect. They said that there were some factors that affected their attitudes toward learning English. Those factors are language curiosity and the teacher. The participants first mentioned that they liked English because of the language, especially the uniqueness of the language. This naturally happened to the learners since they were driven by their inborn curiosity to explore the world and the learning experience [69]. If attitudes are negative, every activity which is related to the course will also be negatively affected. In other words, when a particular participant had a negative attitude, she/he tended not to grasp and learn English. Thus, as teachers, they should change students’ perception about English learning. It could be done by using interesting TT as communication tool and providing more interesting material or different classroom atmosphere to the learners. When the negative changed into positive, it would help the participants to understand the English language easily.The high percentage indicated in all the tables has shown that all participants have positive result (4.22) in cognitive attitude. The learner agreed that English is an important world language to communicate. This was alike to Crystal’s statement [70] that English nowadays comes as a global language that comes to be used by more people than any language. Moreover, the discourse of “Education for All” and the increase in the use of English in the global market have recently added a universalistic dimension to the teaching learning of English [71]. Because of that, many students considered English as world language to communicate, as well as to enable them to get better education.In short, thep-value of 0.001 in the t-test result shows that the two-tailed value was <0.05 (alpha value). This shows that the scores differ much between the significance of 0.05. Therefore, the researcher concluded that there is a significant interplay between TT and English language learners’ attitude in secondary high school. The correlation coefficient or Pearson’s r-value is 0.350 and is shown above the square red box in the table of correlation. This shows a tenuous positive correlation. Although the effect is fair and marginal, people are more likely to perform better the longer they spend taking the test. Learners should be at certain cognitive and affective levels, which are appropriate to the learning they undertake. This present study suggests that learners’ positive attitudes toward an educational institution and their teachers, as well as the courses offered improve the quality of their learning. The utility of a subject to be learned should be stated clearly to learner before its content is introduced. Learners need to internalize the understanding that the learning is life itself, not some variation that is independent from their lives. The present study results in the Ha hypothesis accepted.


## 6. Conclusion and Implications

There are several points to be concluded in this research. First, the findings show that the attitude of respondents that is mostly demonstrated is cognitive and that all learners have positive attitude in learning English. Among the classification of learners’ attitude, cognitive attitude is the most dominant attitude and behavioral and emotional attitudes are minor. Second, the correlation coefficient or Pearson’sr-value, which is 0.350 and is shown above the square red box, shows a tenuous positive correlation between TT with the attitude demonstrated by the learners. However, if the length of the test is extended, it is more likely that the students will perform better.This study of TT has affirmed the importance of this teaching point as a means and management to better achieve the learning goal. Better TT is urgently needed to educate students at senior secondary high schools especially in shaping their attitude to more positive direction. When students involve in conversations with their teachers, they play the role of contributors to their perceived impact. Behavior changes is a manifestation of the results of the influence of teacher communication.When TT becomes an issue and learners’ attitude is in the brinks, a simultaneous anticipation needs to be endeavored. The importance of TT in classroom may to a great extent affects the whole process of the teaching and learning. This includes the behavior changes of the learners, the ability they have in achieving better performance as part of the learning goals, as well as the language policy implemented by both the government and the school stakeholders. It is then recommended that these variables will attract more attention from the future researchers to scrutinize the same topics with different contexts and targeted students.



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*Source: 8959615-2023-01-04.xml*