Prediction of Iranian EFL Learners’ Learning Approaches Through Their Teachers’ Narrative Intelligence and Teaching Styles: A Structural Equation Modelling Analysis (Research Paper)

Prediction of Iranian EFL Learners’ Learning Approaches Through Their Teachers’ Narrative Intelligence and Teaching Styles: A Structural Equation Modelling Analysis

^[1]Arezoo Golabi

^[2]Farrokhlagha Heidari*

IJEAP- 1905-1372

Abstract

It goes without saying that there are many influential factors affecting the success of any learning experience, and teachers are definitely among the significant factors influencing the process of teaching and learning. In this respect, the present study sought to investigate the prediction of Iranian English as a Foreign Language (EFL) learners' learning approaches through their teachers’ narrative intelligence and teaching styles. The participants comprised 50 high school teachers along with 400 students in Birjand, South Khorasan, Iran. The necessary data were obtained through Narrative Intelligence Scale, Teaching Style Inventory, and Study Process Questionnaire. The Structural Equation Modeling (SEM) analysis demonstrated that teachers' teaching style had a direct and meaningful effect on learners' deep (β = .31; p= .04) and surface (β = .18; p= .04) learning approaches. Whereas the teachers' narrative intelligence only had a direct and meaningful effect on deep learning approach (β= .006; p= .03) and out of the subscales of narrative intelligence, only narration with a positive effect (β= .28; p= .04) was the best predictor of deep learning approach. Among the five types of teaching styles, the facilitator style with a positive effect (β = .37; p= .001) and the formal authority style with a negative effect (β = -.22; p= .01) were the best predictors of deep learning approach. Besides, the expert style with a negative effect (β =-.14; p= .02) and the formal authority style with a positive effect (β= .16; p= .008) were defined as the best predictors of surface learning approach.

Keywords: Learning Approach, Narrative Intelligence, Teaching Style, Structural Equation Modeling

1. Introduction

Nowadays, teachers are no longer viewed solely as the transferors of knowledge to learners. Instead, they have a prominent role in assisting learners to achieve educational goals in the teaching-learning process (Anderson,2004). Awareness of different characteristics of English as a Foreign Language (EFL) teachers like Narrative Intelligence (NI) and teaching styles will shed light on teaching behaviors which supply important insights into the nature of language learning. Narrative Intelligence proposed by Bruner (1986, 1996 (is based on 2 modes of thought, paradigmatic and narrative, which are applicable for us in understanding our surroundings. The first one is utilized in formal education especially science and philosophy and the second one is mostly common in literature and life. Individuals make sense of the world by narrative and they may have a biological potentiality for understanding the world through narrative (Bruner, 1991). People resort to narrative as a version of reality (Bruner, 1991). This version of reality differs from person to person, so every human being may construct his own narrative based on his conception of life (Bruner, 1991). Bruner considered this capacity as a kind of intelligence (Ranadall, 1999). Randall (1999, p.13) defines NI as “the capacity both to formulate (compose, narrate) and to follow (understand, read) the story of our own life”. He believes that NI is essential for making meaning across the life span. Randall (1999) mentions that NI develops during our life span in tandem with three intelligences proposed by Gardner (1993): inter-personal, intra-personal and linguistic. He stated that improvement of NI which needs the knowledge of words (linguistic intelligence) can be obtained by participation and interaction with others. Our parents navigate us into the world of language by telling stories of others (inter-personal intelligence) and also stories of our own life (intra-personal intelligence).

Randall (1999) classified NI into five components including emplotment, characterization, narration, genre-ation, and thematization. Emplotment includes gathering of past and present events, connecting events, comprehending the environment, conditions, interactions, and prioritizing what is important. Characterization is to characterize ourselves and others in a form of working picture based on a variety of cues and clues. The ignorance of subjectivity in characterization is important as Randall (1999) stated. Bruner (1991) considered this matter as “ subjunctivise” reality. Narration is considered as the most important feature of narrative intelligence by Randall (1999). The narration ability determines the communication with others about the ongoing and present events in a way that they figure out a logical link between and among happenings. Genre-ation is defined as the ability to order events based on predictability like tragic, comic, etc. Finally, thematization refers to the ability to affirm the occurrence of some special events by noticing their meaning and to perceive the main idea of the story.

Teaching styles can be considered as distinct features of teachers to help learners burgeon their potential ability in a learning situation. As mentioned by Borg (2006), teachers should access to a full understanding of teaching styles to collaborate them reform their instructional practices and to appreciate their learners to improve their learning approaches. Grasha (1996) defined teaching styles as the patterns of belief, knowledge, performance, and behavior of teachers when they are teaching. They can influence the character of the learner, the learning environment and totally the execution of the learning process in a classroom. Teaching styles are multidimensional because they affect how teachers present the information, handle their tasks in the classroom, interact and mentor the students and socialize them in their field for the future. Grasha (2003) also considered them as the continuous and consistent behaviors of teachers in their interactions with students during the teaching-learning process.They can also demonstrate the teachers’ preferences during the instructional activities, and comprise methods by which teachers convey information and affect students’ behavior towards understanding and learning (Irby, 1995).

Grasha (1996, p.154) suggested five teaching styles including expert, formal authority, personal model, facilitator,and delegator. An expert teacher has the responsibility of providing correct information for learners. Indeed, he/she is informed in any field which is favorable for every learner. In formal authority, the teacher possesses the role of a faculty member who accentuates plausible, standard, and correct ways to do things and the needed structures for learners are trained to them by teachers. In personal model, the teacher functions as a model and encourages learners to apprehend and exploit one special approach which is acceptable in teacher's view. In facilitator style, the teacher is a leader who leads learners by “asking questions, investigations, suggesting alternatives, and cheers them to develop criteria to make informed choices which develop the capacity for independent action, initiative, and responsibility for learners. In delegator style, the teacher tries to develop learners’ autonomy. Learners do their own works independently and teachers help them just when it is needed.

Grasha (1996) believed that every teacher has each of these teaching styles to some degrees. Hence, he mentioned four clusters of teaching styles which were dominant among teachers. Cluster 1 (expert/ formal authority), cluster 2 (personal model/ expert/ formal authority style), cluster 3 (facilitator/personal model/expert style), and cluster 4 (delegator/facilitator/ expert style). According to Grasha (1996), each cluster of teaching style transfers a distinct message to learners which can determine the mood of the class.

One of the factors which can affect good learning is the approach learners adopt in the learning process which can identify the quality and quantity of learning. The choice of learners for a particular approach is under the influence of many factors such as the nature of the course content and assessment, the method of teaching and the learners’ realization of the relevance and interest of the course (Gibbs, 1992; Ramsden, 1987). Marton and Säljö (1976), for the first time, divided learning approaches into two categories of deep and surface. Based on their classification, the main concern of learners who adopt deep learning approach is meaning while those who prefer a surface learning approach only concentrate on memorization of the parts which may be questioned about. Tickle (2001) realized that learners with deep approach preferences are mastery-oriented. Whereas learners who adopt surface approach only focus on passing aspirations and their effort for learning is at the minimum level, and most of the time their priority is rote learning. Overall, it can be stated that deep approach includes searching for meaning and joining new information with prior experience; on the other hand, the surface approach involves rote memorization of information (Entwistle & Ramsden, 1983).

Consequently, as touched upon earlier, the importance of these variables in language learning process pertaining to both teachers and learners provides sufficient impetus for the present study to investigate the prediction of Iranian EFL learners’ learning approaches through their teachers’ narrative intelligence and teaching styles.

2. Review of the Literature

2.1 Narrative Intelligence

There is a belief in the realm of developmental psychology that human beings make sense of the world around them through narratives (Bruner, 1987; Randall,1999). They can organize their experiences and apply them in their interactions and learning processes via narratives presented to them in the form of a story. So, NI ability is expected to be necessary for the learning process to improve learners’ meta-cognitive ability for comprehending and constructing their own stories (Rowe, Mcquiggan, Mott, & Lester, 2007). The concept of NI has been explored in the work of different researchers. Pishghadam, Golparvar, Khajavi, and Iranrad (2015) investigated the relationship between EFL teachers’ NI and their pedagogical success. They found out that there exists a significant association between these two variables and also genre-ation turned out to be the best indicator of teacher success. In another study, Pishghadam, Golparvar, and Khajavi (2012) examined the role of NI in the success of EFL teachers with respect to major and gender. They concluded that teachers who majored in English Literature represented a higher level of NI. In addition, no statistically significant difference was found between males and females in terms of NI. The relationship between EFL teachers’ effectiveness and their NI was studied by Rezaei Khodkhili and Mall-Amiri (2015). The data analyses revealed that there was a significant relationship between participants’ Persian and English NI and their pedagogical success. Royaei, Ghonsooly, and Ghanizadeh (2014) explored the probable impact of NI on self-regulation among Iranian EFL teachers. The findings demonstrated that NI correlated positively with teachers’ self-regulation. The relationship between NI and language learning was investigated by Pishghadam and Motakef (2012). They stated that narration was significantly indicative of language scores. Khaghaninejad and Chahibakhsh (2015) investigated the possible effects of employing English movies and novels in English classes for improving NI and General English proficiency (GEP) of Iranian advanced learners. The results showed that both novels and movies improved NI and GEP;  moreover, movies were more successful at improving both NI and GEP level of participants.

2.2 Teaching Styles

Everyone has his/her own preferences or styles for doing things. Brown (2000, p.119) defines styles as “common characteristics of rational functioning (a personality kind as well) which pertain to you as an individual and differentiate you from others”. Teaching style is a symptom related to different attainable and identifiable sets of teachers’ behavior regardless of the content that is being taught (Conti & Welborn, 1986). Khany and Tarlani Aliabadi (2016) investigated the possible relationship among teachers’ teaching styles, teachers’ perceptions of their self-efficacy, and learners’ learning styles. They also tried to identify the best predictor of learners’ final achievement. The SEM analysis demonstrated that the variables contributed directly and indirectly to final achievement and the efficacy in classroom management was the best and most powerful predictor of learners' final achievement. The relationship between Iranian EFL teachers’ brain dominance, teaching experience, and their teaching style was explored by Mazaheri and Ayatollahi (2019). Results indicated that there was a significant relationship between the mentioned variables and it was found that EFL teachers’ brain dominance was a better predictor of their teaching style than their teaching experience. Fakhraee Faruji (2012) examined the dominant teaching styles utilized by teachers in private language institutes in Iran; besides, the relationship between using the dominant teaching style and the variables of sex, age, and years of teaching experience was explored. The findings revealed that the formal authority style was the most dominant teaching style while the delegator style was the least dominant one. Furthermore, there was a relationship between the formal authority style and the variables of sex, age, and years of experience as it was applied mostly by male, younger, and low experienced teachers. Sanje and Varnali (2014) studied the effects of teaching style and internet self-efficacy on instructors’ attitudes toward online education. The results demonstrated that the delegator teaching style had a positive relationship, whereas the expert teaching style had a negative relationship with attitudes toward online education. It was also found that internet self-efficacy and the delegator teaching style could predict attitudes toward online education. The investigation of the relationship between EFL teachers’ teaching styles and their autonomy was conducted by Baradaran and Hosseinzadeh (2015) who indicated that there was a significant relationship between teachers’ expert, personal model, and delegator styles and curriculum autonomy. Razak, Ahmad, and Mohd Shah (2007) investigated the perceived and preferred teaching styles of English for Specific Purposes (ESP) students. The findings revealed that the expert, personal model, and delegator styles were the most dominant teaching styles of teachers perceived by the students while the facilitator style was preferred mostly by students. Moreover, the results displayed statistically significant higher scores in terms of preferences for formal authority, personal model, facilitator, and delegator styles.

2.3 Learning Approach

Learners study for specific reasons and really their reasons illustrate the type of approach they adopt. The approach deployed by learners for accomplishing different tasks will be a function of the nature of the task (Laurillord, 1984; Ramsden, 1984). Smith (2005) studied the learning approaches of  248 learners studying Psychology and Business. It was found that Psychology learners adopted deep motive and deep strategies compared with Business learners. Ladan et al. (2014) investigated the learning approach of undergraduate students at Ahmadu Bello University, Zaria. The results indicated that the predominant learning approach among those students was the surface approach. However, deep approach could significantly predict high academic achievement. Personal, family, school, peer, and social factors were also identified as factors affecting the learning approach of students. Floyd, Harrington, and Santiago (2009) examined the effect of engagement and perceived course value on deep and surface learning strategies. The findings demonstrated that course value had a positive and meaningful effect on deep learning and involvement of students. Deep learning approach as the final goal would be achieved by burgeoning the content value and involvement. Kember (2000) carried out a survey on misconceptions about learning approaches, motivation and study practices of an Asian student. He understood that the use of deep and surface approach pertains to the nature of the assessment task and course requirements. The relationship between students’ assessment preferences and their approaches to learning was inquired by Gijbels and Dochy (2006). It was found that the variables were positively related to each other.

Despite a plethora of research examining the relationship among different variables, including teachers’ teaching styles, NI, and learners’ learning approaches, the predictive power of teachers’ teaching style and NI with regard to learners’ learning approaches has received little attention. The current study strives to bridge the alleged gap in the literature relating to the identification of Iranian EFL learners’ learning approaches through their teachers’ NI and teaching styles. Therefore, this study seeks to answer the following questions:

Research Question 1: Can Iranian EFL teachers’ narrative intelligence predict their learners’ learning approaches?

Research Question 2: Can Iranian EFL teachers’ teaching styles predict their learners’ learning approaches?

Research Question 3: Which type of Iranian EFL teachers’ teaching style is the best predictor of deploying a deep learning approach by their learners?

Research Question 4: Which type of Iranian EFL teachers’ teaching style is the best predictor of deploying a surface learning approach by their learners?

Research Question 5: Which component of Iranian EFL teachers’ narrative intelligence is the best predictor of deploying a deep learning approach by their learners?

Research Question 6: Which component of Iranian EFL teachers’ narrative intelligence is the best predictor of deploying a surface learning approach by their learners?

3. Methodology

3.1 Participants

The first group of the participants comprised 400 Iranian EFL learners including 279 females (69.8 %) and 121 males (30.3%) from different high schools in Birjand, South Khorasan, Iran. Their age ranged between 13-18 ( M= 15.65, SD= 1.46). This sample was used due to accessibility. Besides, 50 English teachers were recruited to participate in this research. Each teacher was explored in accordance with a group of 8 learners from his/her own class.

3.2 Instruments

3.2.1Narrative Intelligence Scale (NIS)

The Narrative Intelligence Scale (NIS) constructed and validated by Pishghadam, Baghaei, Shams, and Shamsaee (2011) based on Randall’s (1999) guidelines and validated by Rasch model was used for measuring the teachers' NI. The outputs of Rasch model were item reliability of 0.99 and Pearson reliability of .98 and the Cronbach’s alpha for this questionnaire turned out to be .83. It included two separate narrative tasks for measuring narrative performance. Task 1, the personal narrative task, was based on the prompt of telling the story of one’s first day in the elementary school in English. Task 2, the narrative reconstruction task, asked the participants to look at a strip story and narrate it orally. This scale consisted of 35 items each receiving a score of 1 to 5 producing a range of 35 to 175. The computed Cronbach alpha of this scale for the present study turned out to be .75.

3.2.2 Teaching Style Inventory (TSI)

The Teaching Style Inventory (TSI) devised by Grasha (1996) as a useful and precise tool for teachers to raise awareness of their teaching styles contained 40 items in five sections: Expert, Formal authority, Personal model, Facilitator, and Delegator. Each teaching style entailed eight items. A range of scores (1-5) was assigned for each type. The reliability and validity of the items were calculated by Razak et al.  (2007) which indicated a Cronbach alpha coefficient of .88. Computing the reliability index of this instrument for the current research revealed a cronbach alpha coefficient of .78.

3.2.3. Revised Study Process Questionnaire (R-SPQ-2F)

This scale was prepared and validated by Biggs, Kember, and Leung(2001). It consisted of 20 items and utilized a 5-point Likert scale ranging from 1 (never true of me) to 5 (always true of me). This questionnaire had two main scales, Deep Approach (DA) and Surface Approach (SA) with four subscales, Deep Motive (DM), Deep Strategies (DSs), Surface Motive (SM), and Surface Strategies (SSs) each measuring 5 items. Deep and surface approach were estimated as follows: DA= items 1+2+5+6+9+10+13+14+17+18 and SA= items 3+4+7+8+11+12+15+16+19+20. In the current study,  the Persian format of this tool was employed which was validated by Ghanizadeh and Allahdadi (2015) for the context of Iran. The reliability of the Persian version assessed via Cronbach alpha was .69. The Cronbach's alpha approximations for each factor ranged from  .61 to .65. (DM = .68, DSs= .61, SM= .62, SSs= .65). The reliability of DA was 0.77 and it was .66 for SA. The value of cronbach alpha of the Persian version of this scale for our sample was .71.

3.3 Procedure

The procedure for data collection was divided into two parts. The first part consisted of self-administered surveys which were targeted at both teachers and learners. The TSI was filled out by teachers and the R-SPQ-2F by their learners. Teachers were allowed to take the questionnaires home and to submit them after a day but the learners filled out the questionnaires in the classroom. The second part was an interview for teachers. The participants were asked to tell the story of their first day in elementary school in English (task1) and take a look at a strip story picture and narrate it orally (task2). Their voices were recorded by the researchers and later rated by themselves and two other experienced teachers based on NIS.

4. Data Analyses and Results

In order to analyze the data and locate the possible relationships among observed and latent variables, the SEM was run. In this study, deep approach, surface approach, and teaching style were considered as latent variables; whereas, different styles of teaching including expert, personal model, formal authority, facilitator, and delegator and NI were inspected as observed or indicators. Table 1 indicates the descriptive statistics of the five teaching styles and narrative intelligence: the mean (M), the standard deviation (SD), minimum (Min) and maximum (Max).

Table 1: Descriptive Statistics of Teachers’ Teaching Styles and NI

According to Table 1, among the five types of teaching styles, the most dominant teaching style was facilitator (M= 3.87, SD= .51), while the least dominant one was delegator (M=3.50, SD= .51). Besides, among the components of NI, narration (M= 4.27) had the highest and emplotment and thematization (M= 4.17) had the lowest means. Table 2 demonstrates the results of the SEM analysis for estimation of deep and surface learning approaches through teachers’ NI and teaching styles.

Table 2: SEM Analysis: The Values of Effective Variables in Estimation of Deep and Surface Learning Approach

Concerning the predictive power of NI for learners' learning approaches, Table 2 indicates that NI with a standardized coefficient of .45 and a statistically significant p-value had a direct and meaningful effect on deep learning approach but not on surface learning approach. This implies that the average score of a learner’s deep learning approach would be increased approximately .45 by each score of his/her teacher’s NI.  It should be mentioned that the standardized coefficient (β) shows the factor loading of each item with respect to the corresponding factor and presents an easily grasped picture of effect size as cited in Ghanizade and Allahdadi (2015).

Furthermore, the obtained results indicate that teaching styles had a direct and meaningful effect both on deep and surface learning approach as their p-values were statistically significant. The only difference between them pertains to impact rate with β= .29 for deep larning approach and β= .15 for surface learning approach. For finding the best predictor of deploying a deep learning approach among the different teaching styles, the results showed that the facilitator teaching style (β= .41, p= .00) and the formal authority style (β= -.02, p= .00) with statistically significant p-values had a direct and meaningful effect on deep learning approach. As it is evident, the effect of the facilitator style was positive whereas the effect of the formal authority style was negative.

To answer the fourth question regrding the best predictor of deploying a surface learning approach among the different teaching styles, Table 2 indicates that the expert style (β= -.15, p= .02) and the formal authority style (β= .14, P= .01) with p-values of p< .05, had a direct and meaningful effect on surface learning approach. The formal authority style had a positive effect while the expert style had a negative effect. With regard to the fifth research question concerning the predictive power of components of teachers' NI for deploying a deep approach, as it is evident in Table 2, narration (β = .28, p= .04) with a p-value of .04 could predict deep learning approach and its effect was positive.

To determine the best predictor of deploying a surface learning approach among the components of NI, Table 2 reports that none of the components of narrative intelligence could predict surface learning approach as their p-values were not meaningful (p>.05). Figure 1 indicates the final SEM model with standardized path coefficient for this study. It can be easily found out which variables are related to each other by the directional path between them. Oval is indicative of latent variable and rectangular is representative of observed or indicator.

Figure 1. The Schematic Representation of the Final Model with Standardized Path Coefficients

5. Discussions and Conclusion

This research was an attempt to investigate the prediction of Iranian EFL learners’ learning approaches through their teachers’ teaching styles and NI. Concerning the first research question, it was concluded that teachers' NI could predict their learners’ learning approach in the way that it had a direct and meaningful effect only on deep learning approach but not on surface approach. The obtained results are in line with Pishghadam et al. (2015) who figured out that there exists a significant association between teachers' NI and their pedagogical success. Findings also support Pishghadam and his colleagues (2012) who came with the conclusion that NI scores of learners are correlated significantly with English, Farsi, and Arabic achievements. Moreover, Rezaei Khordkhili and Mall-Amiri (2015) examined the correlation between EFL teachers’ effectiveness and their NI and concluded that teachers’ NI is an important indicator of their success and it is somehow in line with the findings of the present study. The capability of teachers for beginning, continuing and finishing the class can provide motivation to learners to follow the syllabus accurately without any deviation from the main road of learning (Bruner, 1987, 1996; Randall, 1999) and when a learner develops good feeling toward the context of learning like classroom, it can be said that he or she will probably deploy a deep learning approach.

With regard to the second research question, it was concluded that teachers’ teaching styles had a direct and meaningful effect on both deep and surface learning approaches. The findings of the present study are in line with Khany and Tarlani Aliabadi (2016) who examined the combined effects of teachers’ teaching styles, the teachers’ self-efficacy and the learners’ learning styles on the learners’ final achievement. They found that the studied variables contributed directly and indirectly to learners’ final achievement. Furthermore, this study supports Mazaheri and Ayatollahi (2019) who reported a significant relationship between the variables of teachers' brain dominance, teaching experience, and teaching style with identifying brain dominance as a greater predictor of teachers’ teaching style. Similarly, Baradaran and Hosseinzadeh (2015) investigated the relationship between Iranian EFL teachers’ teaching styles and curriculum autonomy. They found a significant relationship between teachers’ expert, personal model, and delegator styles and curriculum autonomy.

Concerning the third research question, this study demonstrated that among the five types of teaching styles; the facilitator style with a positive effect and the formal authority style with a negative effect were the best predictors of deep learning approach. This may be due to some characteristics of facilitator teacher in terms of monitoring and guiding learners in the process of learning, devoting his/her time and energy for guiding learners in learning process, advising learners by asking questions, probing choices, encouraging them to have criteria for making informed options, and focusing on learners’ aims and needs. The other predictor was formal authority in which the teacher has the role of a faculty member who demonstrates and trains the plausible and correct ways of doing things to learners. Therefore, the learners have less autonomy and this may demotivate them to learn materials deeply.

Regarding the fourth research question, the formal authority style with a positive and the expert style with a negative effect were the main predictors of surface learning approach. As mentioned before, the formal styles are content-focused and teacher-centered and the instruction mostly employs a traditional lecture format. The teacher views himself as source of information and the students as receivers. Since the emphasis is on assimilating the teacher's viewpoints and knowledge, it seems more probable for learners to take a surface approach to learn. In expert teaching style, a teacher possesses knowledge and expertise that students need and strives to maintain status as an expert among students by displaying detailed knowledge. Since the teacher-as-expert attempts to challenge students to enhance their competence (Grasha, 2003), the learners try hard to learn and use the information and this cannot happen by deploying a surface learning approach.

Considering the fifth research question, it was revealed that narration with a positive effect among the five categories of narrative intelligence was the best predictor of deep learning approach. This is in line with Randall (1999) who sees narration at the heart of narrative intelligence. Narration is manifested easily in the behavior of effective teachers as they know how to manage the order of lessons for teaching based on their learners’ needs and also these teachers know how to connect different parts of a lesson by using proper vocabularies and structures. When learners feel that their teachers have the ability to transfer knowledge very well, this gives them the motivation to follow and learn deeply what is taught and this stimulates their final and instant achievements.

With regard to the sixth research question, as NI totally could not predict the learners’ surface learning approach, none of its components could predict surface learning approach. The findings are not in line with Pishghadam et al. (2012) who reported narration as the best predictor of language scores and Pishghadam et al. (2015) who figured out that genre-ation among the subscales of narrative intelligence was the best predictor of teacher success.

In light of the findings of the current study, it can be mentioned that the narrative performance of teachers should be considered as one of the criteria for recruiting teachers in schools. The discussion of narrative intelligence should be entered in the theoretical section of teachers’ curricula of Teacher Training Courses (TTC). It is evident that teachers who know how to transmit knowledge to learners can be more successful. Good teachers like good narrators should have this ability to give their attention and emphasis to their audience and find solutions for possible problems students might face in the process of learning (Gardner, 1993). Eventually, policy makers and head masters should improve student-teachers’ narrative intelligence by providing proper learning environments and teachers should try to improve their narrative intelligence ability. Furthermore, it is an expectation of teacher to utilize various teaching styles in his or her class and modify them. Moreover, it is expected that the teachers use appropriate teaching style and adjust an enjoyable learning context for students based on their needs in order to learn effectively. Vaughn and Baker (2001) believe that utilizing a variety of teaching styles may lead to lifelong learning on students’ part. Vaughn and Baker (2001) also stated that by adopting a variety of teaching styles, teachers can provide themselves with a variety of teaching conditions. Anyhow, the choice of teaching style is under the influence of many factors including 1) the capability of learners for handling the course demands, 2) the learners’ level of proficiency, 3) the ability of teachers in controlling classroom tasks directly, and 4) the inclination of teachers to keep their contact with learners. Teachers should keep this point in mind that this choice may lead to adopting a deep or surface approach by learners in different learning environments. At last, it should be mentioned that since this research was done in different high schools in Birjand, South Khorasan, its results cannot be generalized to other contexts and should be replicated with other teachers and learners both in Iran and with teachers and learners from other L1 backgrounds.