Listening comprehension anxiety (LCA) has received considerable research over the past few decades, but conceptual models to pinpoint the sources of LCA have not been adequately explored. The present study set out to probe the structural model hypothesizing the predictive role of cultural intelligence (CQ) and language learning motivation (LLM) in affecting LCA. To this end, 250 English as a foreign language) EFL) learners completed the three self-report scales measuring the variables under investigation. As for the data analysis, Pearson product moment correlations and structural equation modeling (SEM) were used to examine the interplay among the variables, and to test the fit of the model. The correlation analysis indicated that there was a statistically significant: (a) negative relationship between CQ and LCA, and (b) reverse correlation between LLM and LCA. Moreover, the SEM analysis revealed that CQ and LLM accounted for .39% of the variance in LCA. Specifically, CQ abated to − .33% of the variance while LLM decreased to − .29% of the variance in LCA. Additionally, it was established that while both predictors had a significant effect, LLM appeared to be a robust predictor of LCA reduction. This implies that students who had a higher level of LLM were less likely to experience LCA. Therefore, materials developers, curriculum planners, and language teachers, in particular, are advised to train learners to foster CQ and LLM, as both are known to reduce LCA. Detailed discussion of the results and implications of the study are provided in the paper.
Research Article
Effect of Learners’ Cultural Intelligence on Listening Anxiety: Toward Testing a Model with the mediating role of Language Learning Motivation
https://doi.org/10.21203/rs.3.rs-3572352/v1
This work is licensed under a CC BY 4.0 License
You are reading this latest preprint version
Conceptual Model
Cultural intelligence
EFL learner
Language learning motivation
Listening comprehension anxiety
Structural equation modeling
Learning a second language (L2) is influenced by a variety of factors, including motivation. It is widely acknowledged that this factor provides the primary impetus for L2 learning (Alamer 2022; Rayan & deci, 2017; Dörnyei 2020; Dörnyei & Ushioda 2021). Dörnyei (2005) questioned the role of the integrative variable in motivating students. Dörnyei maintained that learners are unable to acquire an L2 within the EFL context due to the absence of direct contact with speakers of the target language. To Dörnyei, this fact diminishes the impact of the integrative orientationF. Previous empirical studies (Hariri et al., 2021; Shirzad et al., 2020; Zaremarzoni et al. 2022) have indicated that L2 achievement is highly correlated with motivation. They identified that learners’ motivation and language achievement are positively and significantly correlated. Besides, various theoretical assumptions acknowledged motivation as a predictor of L2 achievement. For example, the motivational process model proposed by Alamer and Lee (2019) is comprised of different psychological factors drawn from the achievement emotion theory (Pekrun et al., 2009), self-determination theory (SDT) (Ryan & Deci, 2017), and goal-orientation theory (Murayama & Elliot, 2019) to elucidate L2 achievement. In their view, language emotions and L2 achievement were interrelated through autonomous motivation. They pinpointed that motivation may not only influence learners’ L2 attainment directly, but may also influence other factors (e.g., emotions & cognitions) as well. Professional literature acknowledged that learners with low orientation may not sustain to follow the goal of L2 learning. They believe that learners’ orientation toward L2 learning can formulate dynamic interplay concern their motivational and linguistic outcomes. Therefore, they considered motivation as a driving force to accomplish learners’ objectives of language learning (Alamer, 2022; Deci & Ryan, 1995; Hariri et al., 2021; Pekrun et al., 2009; Vallerand, 1997).
Moreover, some evidence (e.g., Ang & Van Dyne, 2008; Gedik Bal, 2022; Lambert Snodgrass et al., 2023; Sternberg et al., 2021) have acknowledged the predictive role of CQ in promoting L2 learning achievement. Ang and Van Dyne (2008) conceived CQ as a multidimensional trait in educational psychology that influence language learning as an external factor. Sternberg et al. (2021) conceived CQ as learners’ capability to manipulate problems arising in interactions with peers. Furthermore, CQ is thought to contribute to psychological well-being, intercultural adjustment, and job adaptation (Ang, et al., 2020). In their study, they found that CQ plays an increasingly crucial effect in the L2 learning process. In line with recent theoretical developments in L2 learning (e.g., Alamer, 2022; Hariri et al., 2021; Sternberg et al., 2021; Snodgrass et al., 2023), motivation and CQ are assumed to be dynamic affective and cognitive situation-specific constructs. To test the effects of both constructs, this study hypothized a model to probe the role of cognitive and affective factors (i.e., CQ & LLM) in LCA. LCA, often intertwined with speaking, is an anxiety-provoking factor that can challenge L2 listening achievement (Liu, 2021). Maclntyre and Gardner (1994, p.24) conceptualized foreign language anxiety (FLA) as “the feeling of tension and apprehension, specifically associated with L2 contexts, including speaking, listening, and learning”. Accordingly, LCA is conceptualized as anticipating fear, tension, worry, and feeling unsure about listening, and inability to manipulate the messages sent by others due to anxiety, the fear of being misinterpreted, inappropriate processing of the messages (Kim, 2000; Kimura, 2008; Wheeless, 1975). Dörnyei (2005) categorized LCA as a psychological factor that influence learning process. Recently Teimouri et al. (2019) found that learners whose motivation is controlled appear to be less motivated to learn than those with an uncontrolled type. Currently, practitioners begun to test the interplay between LCA and other affective factors like learners’ enjoyment and anxiety (Dewaele & Dewaele, 2020), cultural background and listening comprehension (Al-khresheh, 2020), listening task types (Authors, 2019), listening self-efficacy (Fathi et al., 2020). However, the relationship among CQ, LLM, and LCA has yet to be examined. Therefore, the current study aimed to propose a model to predict LCA. Accordingly, the objectives guided this study is to (a) identify a relationship among the variables under study, (b) uncover whether CQ with mediating role of LLM affects learners’ LCA, and (c) determine if CQ has a significant direct and indirect impact on LCA. Consequently, it has been hypothesized that CQ contributes to a decrease in LCA, which in turn promote students' listening performance. Particularly, it is posited that CQ has a direct impact in LCA. Moreover, students' CQ with the mediating role of LLM may significantly influence LCA. In spite of the rich literature (e.g., Ang et al., 2020; Gedik Bal, 2022; Sternberg et al., 2022; Lambert Snodgrass et al., 2023) supporting the positive effect of CQ on L2 achievement, it was hypothesized that constructs such as LLM, CQ, and LCA have complex and unpredictable associations. Besides, they have been the subject of much empirical work (e.g., Hariri et al 2021; Heidarzadi et al., 2022; Lambert Snodgrass et al., 2023; Sternberg et al. 2022; Yang, 2020; Zaremarzoni et al. 2022) to name but afew. Therefore, a direct or indirect relationship between the variables discussed above may provide exciting results that fill the existing gap in the literature. Thus, the following research questions were addressed:
Research Questions
- How do cultural intelligence, language learning motivation, and L2 listening anxiety relate to Iranian EFL learners' cultural intelligence?
- Does language learning motivation directly influence students’ listening anxiety?
- Does cultural intelligence have a significant direct effect on students’ listening anxiety?
- What are the effects of cultural intelligence on students' listening anxiety when language learning motivation serves a mediator?
Hypothetical model
Based on the L2 literature and theoretical assumptions regarding CQ and LLM as predictors of language anxiety, the following conceptual model is proposed. This hypothesized framework was based on the findings of the related literature (e.g., Ang, et al., 2020; Gedik Bal, 2022; Maclntyre & Gardner, 1994; Dörnyei, 2005; Dewaele & Dewaele, 2020; Liu, 2021; Lambert Snodgrass et al., 2023; Wheeless, 1975). Specifically, the model includes as many as three main constructs (i.e., CI, LLM, & LA) and 14 parameters. In order to test the model, the effects of learners' CQ (i.e., exogenous variable) were considered on LCA (i.e., endogenous variable) with LLM serving as a mediating variable (see, Table 1 and Fig. 1).
|
No. |
Dimension |
Constructs |
Subscales |
|---|---|---|---|
|
1. |
Exogenous |
CQ |
ME, CO, MO, BE |
|
2. |
Endogenous |
LCA |
LSC, T, FNE |
|
3. |
Mediator |
LLM |
A, ER, IR, IDR, IMK, IMA, IMS |
| Note: ME= Metacognitive; Co= Cognition; Mo= Motivation; BE= Behavior; LSC= Lack of self−confidence; | |||
| T= Tension; FNE= Fear of negative evaluation; A= Amotivation; ER= External regulation; IDR= Identified regulation; | |||
| IMK= Intrinsic motivation knowledge; IMA= Intrinsic motivation accomplishment; IMS− Intrinsic motivation simulation | |||
Theoretical Background and the Conceptual Framework
Listening comprehension anxiety
Listening comprehension is regarded by Vandergrift (1997) as a highly integrative skill that enhance the development of other language skills. Horwitz et al. (1986) suggested three constructs for of FLA: (a) communication apprehension, (b) FNE, and (c) test anxiety. In communication apprehension, a person is apprehensive of communicating with other individuals, which may manifest as a shyness towards others. In FNE, students avoid situations in which their performance might be negatively evaluated. Finally, learners may have test anxiety due to the stress of tests or academic evaluations. In addition to these elements, some researchers (e.g., Dewaele & Dewaele, 2020; Dörnyei, 2005; Ji et al., 2022; Oteir & Al-Otaibi, 2019) affirmed that FLA encompasses beliefs, behaviors, and feelings resulting from the overall language learning process both inside and outside of the classroom. As a relatively new concept, LCA was viewed as a subtype of FL anxiety, as it was considered a different type of anxiety. Wheeless (1975) has provided one of the earliest definitions of LCA. Wheeless described LCA as “a receiver’s apprehension, fear of misinterpretation, inadequately processing or not being able to adjust psychologically to messages sent by others” (p.263). Several theoretical and empirical works (e.g., Gonen, 2009; Horwitz et al., 1986; Kim, 2000l; MacIntyre & Gardner, 1994) have investigated the causes of LCA among EFL students. Based on Kim's (2000) research, it appears that foreign language listening anxiety is largely caused by the characteristics of the listener and the characteristics of the text. However, Gonen (2009) believes that various factors contribute to LCA, including the speed of delivering a message, the clarity of the listening text, and the lack of visual aids. Three approaches (i.e., psychological, situation-specific, and social) have been proposed to define LCA. MacIntyre and Gardner (1994) have acknowledged that these tripartite notions formulate the theoretical model of LCA (See, Fig. 2).
Anxiety was conceptualized from the psychological perspective (i.e., macrosystem level) as heightened activity of the self-governed system and feelings of tension and anxiety (Spielberger, 1971). This level accounts for psychological mechanisms of different sorts of anxiety including LCA. Secondly, social approaches (i.e., mesosystem level) to listening anxiety assert that listening anxiety occurs when listeners create a false impression that they must comprehend everything they hear (Wheeless, 1975). In addition, the situational approach (i.e., microsystem level) suggested that FLA differs from other anxieties and that certain classroom settings may be conducive to an increase in FLA (Horwitz et al., 1986). This model clarifies the idea that there is no clearcut restriction in terms of theoretical conceptualization of LCA. Of all levels, the psychological level to LCA was found to be the most common adopted, followed by the social and the situation-specific level (Ji et al., 2022). The notion that the common adopted approach was the psychological perspective (i.e., macrosystem level) implies that LCA is not specifically well defined (Ji et al., 2022). Accordingly, voluminous research seemed to be influenced by the gloomy border of the theoretical underpinning of LCA, causing them to take the vast way to conceptualize it.
Cultural Intelligence Theory
The term CQ, ability to function effectively in an intercultural environment, was initially proposed by Earley and Ang (2003). Drawing on Stern's theory of general intelligence, Ang et al. (2007) conceived CQ as "individuals' ability to function effectively in diverse settings"(p. 337). CQ, according to some practitioners (e.g., Ang et al., 2007; Hampden-Turner & Trompenaars, 2006), is the ability to comprehend, reason, and behave efficiently in cultural and social settings. Brislin et al. (2006) cited a number of these skills: (a) the ability to accept cross-cultural confusion, (b) observe cultural values without judgment, and (c) recognize cultural differences. Ealy and Ang (2003) develop the CQ framework to measure both mental activity and the social interaction. The mental activity comprised three facets measuring cognitive, metacognitive, motivational and the social aspect measures behavior as the social interaction. The framework was developed to determine learners' conceptions of their capacity to reason cross-culturally and solve real-world problems. In cognitive aspect, learners are asked to evaluate their understanding of cultural norms, conventions, and practices. Cognitive CQ norms are conceptualized by Van Dyne et al. (2012) as a variety of knowledge such as politics, legislation, art, history, geography, and norms for social interaction. Next, the metacognitive aspect helps learners understand cultural differentiations effectively. Ang and Van Dyne (2008) posit that metacognitive aspect concerns learners’ level of cultural awareness. Van Dyne et al. (2012) classified metacognitive CQ into: (a) planning, (b) awareness, and (c) checking sub-factors. Individuals consider the upcoming intercultural experience in their planning process and estimate possible difficulties. The concept of awareness is interrelated to the understanding of how culture influences communication in real-time. And, checking involves the modification of assumptions and inferences based on actual experiences. Finally, the expectancy-value motivational aspect is extracted from the theory of motivation. An essential component of this construct is the ability to concentrate energies on learning about and navigating situations involving cultural diversity as well as being able to function within them. A behavior-focused approach to cross-cultural competency focuses on communicating effectively with people from other cultures requires appropriate verbal and non-verbal behaviors. To Van Dyne et al. (2012), behavioral phase includes speech acts, verbal and non-verbal behavior. The current study does not cover this aspect.
Language Learning Motivation
Motivation has been cited by many practitioners (e.g., Alamer & Lee, 2019; Pekrun et al., 2009; Dörnyei, 2005; Ryan & Deci 2017; Shirzad et al., 2020) as an indirect and direct predictor of L2 attainment. Gardner (1985) defined motivation as “the combination of effort plus desire to achieve the goal of learning the language plus favorable attitudes toward learning the language” (p. 10). Dörnyei (1996) conceptualized motivation as both personal (e.g., experiences, growth, and achievements related to prior learning) and social matters (e.g., attitudes of language in terms of its status or power) that affect learners’ perceptions toward learning a language. Later, Dörnyei (2005) suggested the L2 motivational self-system to elucidate the notion of possible, ideal, and ought-to selves. In such a conceptualization of motivation, Dörnyei focuses on why, how long, and how difficult it is to understand human behavior. This definition is different from Deci and Ryan’s (1995) SDT who distinguished two general types of motivation (i.e., intrinsic & extrinsic). They postulated that the former is founded upon inborn needs to do something because it is inherently satisfying in the activity per se either for fun (identified orientation), or an impression of autonomy in learning. However, the latter is an external orientation (i.e., taking action to achieve external rewards, pressures, and expectations) which is controlled due to some external regulators like family, teacher, or society (i.e. introjected orientation). Following Deci and Ryan’s classification, Vallerand (1997) distinguished a three-part taxonomy of external motivation according to which they are embraced into the self-concept: (a) external, (b) introjected, and (c) identified regulation. A person's external regulation is based on benefits or costs that come from outside the person. It is for a more immediate bonus, prospect, or expectations (Gardner & Macintyre, 1991). Another form of extrinsic motivation is introjected regulation. It refers to reasons that have been incorporated into individuals' self-motivation. Introjected regulation motivates learners to do things not because they want to, but because they feel guilty about not doing them. Finally, among all forms of extrinsic motivation, identified regulation stands out as the most self-determined (i.e., the most autonomous behavior). In this situation, due to its importance for accomplishing a goal, students would perform the task. Besides, Vallerand proposed three facets of intrinsic motivation: (a) knowledge, (b) accomplishment, (c) stimulation. Knowledge is the motivation for the feelings associated with learning new things. The feeling of accomplishment refers to the sensation of mastering an activity or complying an objective. Finally, stimulation relates to motivation based on the sensations generated by accomplishing a task. Deci and Ryan (1995) compared all sorts of motivation with amotivation. It is a situation in which individual sees no interaction between their actions and the results of the performance. (Abramson, Seligman, & Teasdale, 1978). In this study, we utilized Vallerand (1997) classification of motivation as extracted from SDT to determine participants’ orientation in all analyses.
Participants
From a population of English-language private institutes, a number of 250 students were selected at random. In order to select a representative sample, stratified random sampling and cluster sampling were used. Several clusters were included in the sampling process (e.g., cities, districts, language schools, majors, genders, and ages). Participants included both males (N = 117) and females (N = 133) and their ages varied from 16 to 23 (M = 17.12, SD = 5.32). Their language learning experience varied from 1 to 3 years (M = 2.05, SD = 4.26). In order to minimize the bias effect, 23 private English language institutes were randomly chosen from three districts in Tehran and Karaj for the sampling multistage study.
Data Collection Procedure
This study probes testing a model to determine whether LLM mediates the predictive effect of CQ on either decreasing or increasing students' LCA. A commonly accepted sample size to deploy SEM approach is a minimum factor of fifteen cases to be estimated (Byrne, 2010). In this study, there are 14 subscales (i.e., LLM = 7; CQ = 4; LCA = 3) that could be estimated for the analysis. In order to satisfy the SEM sampling requirements, this study requires at least 210 participants. To determine a sufficient sample size regarding the parameters (N = 14), and to apply the coefficient of 15 per variable (Hoe, 2008), this study requested 250 EFL learners to fill out the questionnaires. Therefore, 750 questionnaires were considered for the analysis of the data. The number seems to be appropriate due to the fact that some questionnaires may not meet a valid response rate of 95.10%. The scales were distributed online via the Porsline survey platform by the researchers. We requested that teachers and directors allow students to complete the scales simultaneously during their common class time or during their free time in order to obtain a valid response. It took approximately 10–15 minutes to complete each scale. Altogether 682 questionnaires were retrieved but 68 cases were found to be invalid upon the initial exploration analysis due to late or careless response, missing data, and failure to reply. Following Tabachnick and Fidell (2007) guidelines, all the outliers and random errors were excluded. Thus, the data analysis was conducted on the valid questionnaires, of which 94% met the criteria for valid responses. This yielded a rather normally distributed valid responses which seemed to be qualify for the development of SEM measurement.
Instruments and Measures
Cultural Intelligence Scale (CQS)
The CQS measures effective management within a range of culturally diverse environments. The scale, adapted from Ang et al. (2007), contained 20 self-report items in four different components that evaluates (a) metacognitive (MECQ, 5 items), (b) cognitive (CCQ, 6 items), (c) motivational (MCQ, 4 items) and (d) behavioral (BCQ, 5 items) facets. Each sub-factor gauges one aspect of CQS in a 7-point Likert scale ranging from (1) strongly agree to (7) strongly disagree. The results of CQS were reported based on the mean scores (i.e., 20 to 140) obtained by the participants. A higher mean scores indicate a higher level of CQ ability. The tool has undergone a comprehensive validation process, and literature proposes that it can be used in different settings (Van Dyne et al., 2012). In the case of different EFL/ESL learners, Barzykowski (2019) and Ghonsooli and Shalchy (2013) tested the validity and reliability of the scale. The factorial analysis revealed that CQS is satisfactory. In addition, Ghonsooli and Shalchy (2013) piloted CQS to suit the context of Iran (α = 87). An analysis of reliability was also conducted in the present study. the CQS enjoys high reliability (α = .88) indices for this survey at a pilot study (n = 86). For the subscale, the reliability coefficient was as follows: MECQS (α = .87), CCQS (α = .89), MCQS (α = .91) and BCQS (α = .86).
Foreign language listening anxiety scale
In order to assess the EFL learners' LCA, Kim's (2000) scale of L2 listening anxiety was used. The original scale was developed by Kim in Japanese version for Korean EFL learners. The scale was validated among 452 Japanese students. The factor analysis revealed three main constructs in terms of (a) tension, (b) worry over English listening as well as (d) the lack of self-confidence. Kumura (2008) modified the dimensions to emotional, cognitive and anticipatory fear. This study used a three-dimensional scale (i.e., LSC, T, & FNE) in thirty-three statements, ranging from 33 to 165 score. A variety of measures were found to have high internal validity and reliability in various studies (e.g., Kimura, 2008; Wang, et al. 2022). In his study, Kim (2000) reported that the scale had an internal consistency alpha coefficient of .93. The researchers conducted a pilot study involving 68 EFL learners in Iran to investigate the reliability of the scale. A high level of reliability (α = .76) was observed in the results and the subscale: (a) LSC (α = .75), (b) T (α = .77), and (c) FNE (α = .76).
Language Learning Orientations Scale
To measure students' language learning motivation, the scale developed by Noels’ et al. (2000) was utilized. This scale included 21 items on a seven-point Likert scale measuring three underlying constructs including external motivation, internal motivation, and amotivation. The external motivation included 3 sub-factors in 9 items (i.e., external, introjected, and identified regulation), each 3 items. Similarly, intrinsic motivation was measured via three parameters (i.e., knowledge, mastery, and stimulation) in 9 items, each 3. The assessment of amotivation included three items. The high score indicates a high degree of relationship between the suggested reason for studying an L2 and the reasons for study. A factor analysis was run to test the construct validity, and across all subscales, the reliability index, ranging from .67 to .88, was acceptable (Noel et al., 2000). Precisely, the instrument has been proven to be both valid and reliable (α = .82) in assessing amotivation, ER (α = .75), and IR (α = .67), IDR (α = .84), IMK (α = .85), IMA (α = .88), and IMS (α = .85). For the current study, the Cronbach’s alpha value was found to be (α = .82, N = 73) acceptable in EFL context of Iran.
Data Analysis
A non-experimental correlation research design, descriptive in nature, was carried out for the purpose of this study. The SEM analysis was performed using the SPSS and AMOS 21 software packages. The SEM requires that different goodness-of-fit values are reported and evaluated in order to assess any model (Hu & Bentler 1999). Thus, two types of metrics (i.e., chi-square/degree of freedom ratio & goodness-of-fit index) were used in the analysis to predict the sources of LCA. Generally, there are three categories of approximation errors: Root mean square error (RMSEA), Tucker-Lewis index (TLI), and comparative fit index (CFI). In line with Bentler and Yuan (1999), values in the region of .95 shows a satisfactory model fit, while values around .90 may be appropriate. To be considered acceptable, the RMSEA should both be equal to or lower than .07. To verify the appropriateness of the model, the composite reliability and Pearson correlation matrix were used.
Analysis of Statistical Assumptions for Normality of Variables
To analyze the distribution of the data, Skewness and Kurtosis analysis of the different variables were run (Table 2).
Table 2 Descriptive Statistics of the data and the assumption of normality of variables
|
Constructs |
Skewness |
Kurtosis |
Descriptive Statistics |
|
K–S test |
|||||
|
Statistics |
SE |
Statistics |
SE |
M |
SD |
Statistics |
Sig. |
|
||
|
ME |
.245 |
.171 |
.637 |
.325 |
13.53 |
2.32 |
.735 |
.652 |
|
|
|
C |
.512 |
.171 |
.164 |
.325 |
18.72 |
2.87 |
.704 |
.704 |
|
|
|
M |
.375 |
.171 |
.114 |
.325 |
15.91 |
2.55 |
1.031 |
.238 |
|
|
|
B |
.004 |
.171 |
.005 |
.325 |
14.16 |
2.39 |
.792 |
.558 |
|
|
|
CQ |
.168 |
.171 |
.215 |
.325 |
62.32 |
6.12 |
1.084 |
.191 |
|
|
|
A |
.968 |
.171 |
.271 |
.325 |
8.21 |
.78 |
1.006 |
.264 |
|
|
|
RE |
.227 |
.171 |
.716 |
.325 |
9.44 |
.91 |
1.095 |
.182 |
|
|
|
IR |
.356 |
.171 |
.620 |
.325 |
11.76 |
1.21 |
.851 |
.464 |
|
|
|
IDR |
.021 |
.171 |
.013 |
.325 |
8.37 |
1.03 |
.989 |
.282 |
|
|
|
IMK |
.375 |
.171 |
.085 |
.325 |
10.17 |
1.24 |
1.013 |
.256 |
|
|
|
IMA |
.001 |
.171 |
.371 |
.325 |
9.85 |
.88 |
1.59 |
.136 |
|
|
|
IME |
.001 |
.171 |
.371 |
.325 |
9.85 |
.88 |
1.159 |
.136 |
|
|
|
LLM |
.675 |
.171 |
.915 |
.325 |
65.25 |
8.45 |
.717 |
.683 |
|
|
|
LSC |
.131 |
.171 |
.341 |
.325 |
30.44 |
4.18 |
.959 |
.317 |
|
|
|
T |
.085 |
.171 |
.689 |
.325 |
22.51 |
3.77 |
.932 |
.351 |
|
|
|
FNE |
.220 |
.171 |
.719 |
.325 |
5.09 |
1.01 |
.894 |
.401 |
|
|
|
LCA |
.371 |
.171 |
.512 |
.325 |
58.04 |
6.82 |
.969 |
.305 |
|
|
SE: Standard error; Kolmogorov–Smirnov test = K-S test
Table 2 indicates that distribution of data is normal, and the p values for both K-S and the test of normality for the different variables were found to be larger than .05. Thus, the null hypothesis for the total results was supported. Alternatively, K-S test indicated that the distribution of data did not conform a normal distribution (p > .05). After analyzing the normality assumptions, the researchers derived that the data are normal and make inferential statistics safe based on the result. To answer the first research question, Pearson product moment correlation (Table 3) was conducted to uncover how EFL students perceive their LCA on two measurable aspects (i.e., CQ & LLM).
Table 3 Correlation matrix of CQ, LLM, and LCA
|
C |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
13 |
14 |
15 |
16 |
|
ME |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
CO |
**69. |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
MO |
**77. |
**72. |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
BE |
**66. |
**60. |
**75. |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
|
CQ |
**81. |
**79. |
**78. |
**80. |
1 |
|
|
|
|
|
|
|
|
|
|
|
|
A |
**24. |
**17. |
**19. |
**18. |
**20. |
1 |
|
|
|
|
|
|
|
|
|
|
|
ER |
**18. |
**22. |
**20. |
**20. |
**21. |
**61. |
1 |
|
|
|
|
|
|
|
|
|
|
IR |
**21. |
**19. |
**17. |
**16. |
**25. |
**59. |
**69. |
1 |
|
|
|
|
|
|
|
|
|
IDR |
**17. |
**20. |
**19. |
**22. |
**22. |
**70. |
**63. |
**64. |
1 |
|
|
|
|
|
|
|
|
IMK |
**19. |
**21. |
**18. |
**19. |
**25. |
**64. |
**57. |
**68. |
**62. |
1 |
|
|
|
|
|
|
|
IMA |
**22. |
**17. |
**21. |
**18. |
**27. |
**72. |
**65. |
**70. |
**66. |
**61. |
1 |
|
|
|
|
|
|
IMS |
**18. |
**22. |
**18. |
**21. |
**29. |
**68. |
**64. |
**62. |
**74. |
**64. |
**51. |
1 |
|
|
|
|
|
LLM |
**28. |
**25. |
**22. |
**26. |
**33. |
**77. |
**71. |
**75. |
**80. |
**69. |
**70. |
**66. |
1 |
|
|
|
|
LSC |
**18.- |
**17.- |
**19.- |
**22.- |
**24.- |
**21.- |
**19.- |
**20.- |
**17.- |
**23.- |
**24.- |
**23.- |
**22.- |
1 |
|
|
|
T |
**22.- |
**19.- |
**22.- |
**18.- |
**28.- |
**17.- |
**16.- |
**22.- |
**21.- |
**25.- |
**22.- |
**24.- |
**30.- |
**65. |
1 |
|
|
FNE |
**17.- |
**20.- |
**17.- |
**22.- |
**26.- |
**22.- |
*14.- |
**18.- |
**19.- |
**21.- |
**18.- |
**21.- |
**28.- |
**68. |
**66. |
1 |
|
LCA |
**21.- |
**24.- |
**20.- |
**24.- |
**35.- |
**25.- |
**21.- |
**23.- |
**22.- |
**26.- |
**24.- |
**26.- |
**33.- |
**81. |
**74. |
**86. |
**Significance level .01, *significance level .05
The results listed in Table 3 show a significant negative correlation between the variables of CQ, LLM and LCA. As observed in Table 3, a negative correlation was discovered between total CQ and LCA (r = -.35, n=250, p < .01). Moreover, he results indicated a significant negative association between LLM and LCA (r = -.33, n=250, p < .01). Based on the results, it appeared that the correlation coefficient between total LLM and LCA was greater than that of CQ and LCA. Intestinally, both CQ and LLM had a negative relationship with LCA. Of the two variables, the findings revealed that LLM enjoyed more predictive power than CQ. This implies that high level of LLM will decrease learners’ anxiety in listening comprehension.
Detailed analysis of the research questions two and three
To uncover if students' LLM and their CQ significantly affect LCA, the final conceptual model was modified by fixing the free parameters. Notably, after the review of the initial model, it was determined that the model should be modified in some indicators so that the fit obtained is suitable according to the limits of the criteria. The results of absolute, adaptive and frugal indicators show the final model is at an appropriate level. Thus, the revised measurement model required further analysis. Table 4 shows the fit indices obtained from the analysis of data and variables after two steps of correction.
Table 4 Goodness-of-fit indices for the fitted model
|
Values before modification |
Optimal values |
Fit goodness indicators |
Indicator |
|
622.566 |
Nil |
χ2 |
Absolute |
|
.999 |
≥.90 |
GFI |
|
|
.999 |
≥.90 |
AGFI |
|
|
.999 |
≥.90 |
NFI |
Comparative |
|
.998 |
≥.90 |
CFI |
|
|
.993 |
≥.90 |
TLI |
|
|
.992 |
≥.90 |
RFI |
|
|
.511 |
≤. .5 |
PNFI |
Thrifty |
|
.037 |
.08≥ |
RMSEA |
|
|
2.478 |
3≥ |
(χ2/df) |
|
|
202 |
0≤ |
Df |
|
|
.001 |
.05≥ |
p value |
As indicated in the Table, it appears that the model obtained after correction has acceptable fits based on the statistical analysis of the three comparative, absolute, and parsimonious indices. Furthermore, the model is correct in terms of errors and variances. The results of indices revealed a good fit (X2 /df = 2.478; GFI = .999; CFI = .998; NFI = .999; TLI = .993). Also, the chi-square value of the degree of freedom (X2 = 2.478) is between 1 and 3, and the indices (i.e., GFI, CFI, and NFI) are almost equal and greater the critical point (≥ .90) which show that the research variable measurement model is appropriate. Following the guideline proposed by Hu and Bentler (1999), the RMSEA goodness-of-fit index obtained for the proposed model illustrates a marginal value (RMSEA = .037, .08. ≥.) which indicates that the mean of the squared errors of the model is suitable and the model is acceptable (see, Table 5).
Table 5 shows that the coefficient for both CQ and LLM are statistically significant: CQ (t = 4.546, p <.05), LLM (t = 3.978, p <.05). Specifically, CQ had -.33 % of the common variance (R2 =-.152), and LLM demonstrated -.29% of shared variance (R2 = -.117). Consequently, it can be argued that LLM was a more powerful correlate of LCA than CQ.
Analysis of the fourth research question
The bootstrapping regression model was used in order to determine whether the CQ reveals a reverse effect on LCA when LLM serves a mediator. Using the bootstrap method, we can test the indirect effects of parameter estimates by checking the sampling distribution of the estimates. Bootstrapping is a method of resampling a dataset so that different simulated datasets can be generated (Hu & Bentler, 1999). Table 6 illustrates the bootstrapping analysis to test the indirect effects of CQ on LCA with LLM serving as a mediating factor.
Table 6 Bootstrap regression model for the indirect effect of CQ
|
Construct |
B |
R2 |
lower limit |
upper limit |
Sig. |
|
CQ on LCA with the mediating role of LLM |
-.592 |
.393 |
-.641 |
-.453 |
.001 |
As observed in the table 6, the indirect paths according to the standardized values (β = -.592) was followed by the lower and upper limit (i.e., the lower limit= -.641; upper limit= -.453; p < .05). A bootstrap estimate of indirect effect revealed that CQ with mediating LLM on LCA is significant. Figures 3 illustrates a finalized model that has been tested using unstandardized statistics.
Notably, the figure shows finalized standardized model for predicting LCA with indirect paths (i.e., CQ and LLM). As illustrated in the figure, CQ and LLM along with the relevant components reveals the factor loading above (>.30). Moreover, the co-variances in the sub-scales meet the acceptable fit indices. The finalized standardized model indicated that LLM directly influence LCA. Besides, LCA was significantly influenced indirectly by CQ with LLM as a mediator. Thus, according to this hypothesis, LLM may act as a partial mediator between CQ and LCA. In the study, it was found that the direct and indirect paths were able to predict %.39 of LCA.
This study probed the tentative predictive impact of the independent variables (i.e. CQ & LLM) on the dependent variable (LCA). The proposed model aimed to uncover if the level of the subjects’ CQ and LLM can decrease LCA. Initially, this study attempted to explore the interplay among Angs’ et al. (2007) four cultural dimensions, Kim's (2000) three listening anxiety dimensions, and Noels’ et al. (2000) seven LLM dimensions. In terms of the associations among variables, the data revealed a significant relationship. An overall look illustrates a negative association among CQ, LLM, and LCA. Notably, the findings indicated that both CQ and LCA were correlated negatively with LCA. It implies that students with more CQ and LLM are probably perform better in listening skill. The finding indicated the both high CQ and LLM debilitate LCA. To simply put, the findings revealed that high level of CQ and LLM decrease LCA among language learners. More specifically, Iranian EFL learners who scored well on the CQ demonstrate a greater ability to listen effectively. Besides, it was found that students who are highly motivated to learn possess a lower level of anxiety when it comes to listening skills.
The second goal of the current study was to explore direct impact of CQ and LLM on listening anxiety. In term of uncovering the direct effect, all the obtained values were significant and revealed a meaningful prediction. The findings indicated both CQ and LLM had direct effects on endogenous variable. In the present study, LLM was found to be predicted by the seven dimensions. That is to say, students with high level of LLM tend to experience less anxiety in listening skill. This finding is to be discussed with respect to some aspects of LCA. For this study, there were seven sub-scales for LLM. The findings indicated that ER, IDR, IR, IMA, A, IMS, and IMK were important for decreasing listening anxiety. From the theoretical aspect, the findings are in congruent with some authorities in the field such as Vallerand (1997), Dörnyei (1996), Gardner and MacIntyre (1991) who unanimously acknowledged the influential effect of motivation in L2 learning. Besides, the finding is in line with motivational process model (Alamer & Lee, 2019) goal-orientation theory (Murayama & Elliot 2019), and SDT motivational orientations (Ryan & Deci 2017). The findings confirm this point in the literature that motivation affects learners’ attitudes toward learning. Previous studies (e.g., Hariri et al 2021; Zaremarzoni et al. 2022; Shirzad et al., 2020) have strongly acknowledged that the level of motivation plays a significant role in the achievement of L2. This study also postulated that LLM and listening comprehension are positively interrelated. This result echoes the above study in term of the positive association between learners’ motivation and language achievement. While some studies (Alamer & Lee, 2019; Murayama & Elliot 2019; Noel et al., 2000) corroborated that students’ motivation may promote their L2 achievement, the current analysis revealed evidence for the contributing role of external, identified, and introjected regulation. For the current paper, ER was found to be the strongest negative predictors of LCA. Besides, IDR and IR turned out to be the next highest predictors of LCA. The possible justification is that the notion regulated learning, on the one hand, included various processes (i.g., social, cognitive, affective, & motivational) and, on the other hand, is a dynamic process during which learners may set goals, regulate strategies, and manipulate different processes in accomplishing their objectives (Morelli et al., 2022; Sun & Wang, 2020; Zimmerman & Kitsantas, 2002). This results confirms some previous works (e.g., Pintrich, 1994; Zimmerman, 1986) that pinpointed the facilitative role of self-regulated learning in academic achievement. The result echoes Heidarzadi et al. (2022) who found that the high level of self-regulated learning facilitates learners’ language skill (i.e., writing skill in their study). For the purpose of this study, such new pieces of information indicate that the external and identified regulation are important factors in decreasing listening anxiety. Accordingly, this finding suggests that higher external and identified regulation promote listening comprehension ability and may act as a debilitative factor in LCA. This finding may be explained by SDT in which students’ sense of competence may promote motivation for further learning (e.g. Alamer, 2022). Regarding the intrinsic motivation, IMA, IMS, and IMK were found to be the next predictors of LCA. A simple explanation could be increased feeling of accomplishment, simulation, and knowledge in learning a language skill could motivate learners in promoting language skills. In other words, feeling of accomplishment, simulation, and knowledge were predicted by the achievement in a language skill (i.e. listening). This implies that students who have high level of intrinsic motivation are likely to experience less anxiety in listening skill. The findings are in line with Rotgans and Schmidt (2017) who found that learners’ interest and learning are causally linked.
The last goal of this study was to seek whether any of the facets of the CQ can predict listening anxiety among the EFL learners. In so doing, this study probed the indirect effects of CQ on LCA with LLM serving as a mediating role. In the present study, CQ comprised four subscales including the ME, CO, MO, BE. As for cultural intelligence, MO was found to be the highest predictors. The findings indicated that MO, ME, CO, and BE were the strongest negative predictors of LCA, respectively. This finding revealed that the such factors are important for decreasing listening anxiety. This finding accords with several earlier studies (Al-khresheh, 2020; Ang & Rockstuhl, 2021; Ang & Rockstuhl, 2021; Lambert Snodgrass et al., 2023) acknowledging CQ as an important factor in fostering language skill. This piece of information implies that learners who have a high sense of CQ may experience less anxiety and stress in the listening comprehension. ME turned out to be the next predictor of LCA. This result supports some studies (e.g., Chiu et al., 2013; Van Dyne et al., 2012; Yang, 2020) claiming the role of metacognitive awareness in the language skill. For this study, the possible justification is that metacognitive dimension can promote learners’ cultural awareness. Thus, this level of understanding can pave the ground for determining the cultural differences, in particular, and develop listening skill, in general. This finding echoes Van Dyne et al. (2012) who claimed that the concept of awareness intertwined with the understanding of how culture influences communication in real-time. Besides, the findings accord with Yang (2020) and Chin et al. (2022) who posited that ME reflects learners’ awareness and help them interpret, understand their cultural knowledge, and cognitive schema. The last predictors were cognitive and behavioral dimension. Interestingly, they were found to be equally important for decreasing listening anxiety among language learners. This results confirm the point in the L2 literature that teachers should consider learners’ both cognitive and behavioral factors to promote language skill. This is in line with Ang et al. (2007) who argue that CQ can promote diverse cognitive factor and lead a better language achievement. Moreover, this study is in line with Ghonsooli and Shalchy (2013) who concluded students who experience higher level of CQ, their mean scores are significantly higher in cognitive, metacognitive, behavioral, and motivational aspect. However, the results of this study is different from Ghonsooli and Shalchy in terms of the order of predictor. For this study motivational parameter was the first predictors, metacognitive was the second predictor, cognitive and behavioral were equally the last predictors. The possible justification for such a discrepancy may be due the level of the learners, their cultural background, and possibly their cognitive and affective factors.
This study addressed the interplay between CQ and Iranian LCA and added to the body of research on the effect of LLM on the listening achievement. Specifically, this study tested a model with the mediating role of LLM to probe effect of learners’ CQ on listening anxiety. The results indicated the negative associations among CQ, LLM, and LCA as well as the predictive power of the LLM and CQ in reducing listening anxiety. The mediating role of LLM was found to facilitate the negative interplay between seven sub-factors of LLM and three sub-dimensions of LCA. It appears that CQ, LLM, and LCA have a significant negative relationship. Altogether, the findings indicated that both CQ and LLM had a unique contribution on LCA. However, LCA was found to be more strongly predicted by LLM. It is implied that having high level of LLM and CQ best predicts high achievement in listening skill. This means that people with high LLM and CQ are likely to experience less anxiety in listening comprehension. However, of the two variables, LLM seemed to have more predictive power that CQ. The findings of this study have some pedagogical implications for practitioners, language policy makers, and materials developers. As the results indicated, high ER, IDR, IR, IMA, IMS, and IMK turned out to be significant predictors of decreasing LCA. Thus, by directing learners towards the regulation strategies and promoting intrinsic motivation, it is possible to decrease the likelihood of the LCA in the classrooms. Another implication concerns the role of CQ. As the findings revealed, high level of MO, ME, CO, and BE seemed to be the second significant predictors of decreasing LCA. The findings have pedagogical implication for the educational policy makers, language teachers, and those involve in teaching listening skill in that raising leaners’ motivational, metacognitive, and cognitive awareness can possibly promote listening achievement. Specifically, this finding implies that promoting such factors can lead reducing listening anxiety. Therefore, this study suggests that materials developers incorporate various strategies to foster leaners’ CQ. Importantly, this research the finding suggest that teachers of EFL/ESL should also consolidate their educational practices and strategies to encourage learners to develop both LLM and CQ to reduce learners’ anxiety, particularly when it comes to listening. Some limitations should be attended when interpreting the findings. First, although both male and female participated for this attempt, there was no consideration of gender in the analysis. Another study may be conducted to see whether similar results will be obtained for male and female learners. Second, all the participants were recruited from different private language institutes. It is important to exercise caution when interpreting the results in different contexts due to this restriction on the sampling procedure. Finally, this study was conducted using a SEM approach, a replication of the same study may be conducted using log linear modeling to examine the causal relationships among LLM, CQ, and LCA.
Ethical Approval
No need. This declaration is “not applicable”
Funding
No funding. This declaration is “not applicable”
Availability of data and materials
This declaration is “not applicable”
- Abramson, L. Y., Seligman, M. E. P., & Teasdale, I. (1978). Learned Helplessness in Humans: Critique and Reformation. Journal of Abnormal Psychology, 87, 49. http://dx.doi.org/10.1037/0021-843X.87.1.49
- Al-khresheh, M. H. (2020). The Impact of Cultural background on listening comprehension of Saudi EFL students. Arab World English Journal, 11 (3), 349- 371. DOI: https://dx.doi.org/10.24093/awej/vol11no3.22
- Ang, S., & Van Dyne, L. (2008). Conceptualization of cultural intelligence: Definition, distinctiveness, and nomological network. In S. Ang & L. Van Dyne (2008). Handbook on cultural intelligence: Theory, measurement and applications (pp. 3-15). NY: M E Sharpe, Inc
- Ang, S., Ng, K. Y., & Rockstuhl, T. (2021). Cultural intelligence. In R. J. Sternberg (Ed.), The Cambridge Handbook of Intelligence (2nd ed., pp. 820–845). Cambridge University Press. https://doi.org/10.1017/9781108770422.035
- Ang, S., Van Dyne, L., Koh, C., Ng, K., Templer, K. J., & Tay, C. (2007). Cultural intelligence: Its measurement and effects on cultural judgment and decision making, cultural adaptation and task performance. Management and Organization, 3, 335–371.
- Alamer, A. (2022). Having a single language interest autonomously predicts L2 achievement: Addressing the predictive validity of L2 grit. System, 108, 102850. DOI:10.1016/j.system.2022.102850
- Alamer, A., & Lee, J. (2021). Language achievement predicts anxiety and not the other way around: a cross-lagged panel analysis approach, Language Teaching, 25, 1-22. Research doi:10.1177/13621688211033694.
- Authors (2019). Effects of pre-listening task types on the development of EFL learners' listening comprehension ability. International Journal of Listening,37 (2) 156-170. https://doi.org/10.1080/10904018.2019.1654867
- Barzykowski, K., Majda, A., Szkup, M., Przyłęcki, P. (2019). The Polish version of the Cultural Intelligence Scale: Assessment of its reliability and validity among healthcare professionals and medical faculty students. PLoS ONE 14(11), e0225240. https://doi.org/10.1371/ journal. pone.0225240.
- Bentler, P. M., & Yuan, K. H. (1999). Structural equation modelling with small samples: Test statistics. Multivariate Behavioral Research, 34, 181–197.
- Brislin, R., Worthley, R., & Macnab, B. (2006). Cultural intelligence: Understanding behaviors that serve people’s goals. Group and Organization Management 31(1), 40-55.
- Byrne, B. M. (2010). Structural equation modelling with AMOS: Basic concepts, applications, and programming (2nd ed.). Routledge: Taylor & Francis Group.
- Chiu, C.-Y., Lonner, W. J., Matsumoto, D., & Ward, C. (2013). Cross-cultural competence: Theory, research, and application. Journal of Cross-Cultural Psychology, 44, 843–848. doi: 10.1177/0022022113493716
- Chin, T., Meng, J., Wang, S., Shi, Y., & Zhang, J. (2022). Cross-cultural metacognition as a prior for humanitarian knowledge: When cultures collide in global health emergencies. Journal of Knowledge Management, 26, 88–101. doi: 10.1108/JKM-10-2020-0787
- Derakhshan, A. (2021). Should textbook images be merely decorative? Cultural representations in the Iranian EFL national textbook from the semiotic approach perspective. Language Teaching Research,1362168821992264 https://doi.org/10.1177/1362168821992264
- Dewaele, J.-M., & Dewaele, L. (2020). Are foreign language learners’ enjoyment and anxiety specific to the teacher? An investigation into the dynamics of learners’ classroom emotions. SSLLT 10 (1), 45-65. http://dx.doi.org/10.14746/ssllt .2020.10.1.3
- Dörnyei, Z., & Ushioda, E. (2021). Teaching and Researching Motivation (3rd ed.). Routledge
- Deci E.L. & Ryan, R. M. (1995). Human autonomy: the basis for true self-esteem. In: Kemis MH (eds) Efficacy, Agency and Self-Esteem. New York: Plenum Press.
- Dörnyei, Z. (1996). Moving language learning motivation to a larger platform for theory and practice. In Oxford, R. L. (ed.), Language learning motivation: pathways to the new century (89–101).
- Dörnyei, Z. (2020). Research frontiers III: Long term motivation and persistence. Innovations and Challenges in Language Learning Motivation. Routledge
- Dörnyei, Z. (2005). The psychology of the language learner: Individual differences in second language acquisition. Routledge
- Dörnyei, Z., & Ryan, S. (2015). The Psychology of the Language Learner Revisited. Routledge
- Fathi, J., Derakhshan, A., & Torabi, S. (2020). The Effect of listening strategy instruction on second language listening anxiety and self-efficacy of Iranian EFL learners. Sage Open, 10(2)1-13. https://doi.org/10.1177/2158244020933878
- Earley, P. C., & Ang, S. (2003). Cultural intelligence: Individual interactions across cultures. Stanford University Press
- Gardner, R. C. (1985). Social Psychology and Second Language Learning: The Role of Attitudes and Motivation. Arnold.
- Gedik Bal, N. (2022). Cultural intelligence of English language learners and their perceived strengths and weaknesses in intercultural communication. TESL-EJ, 26(2), 1-18. https://doi.org/10.55593/ej.26102a3
- Ghonsooli, B., & Shalchy, S. (2013). Cultural intelligence and writing ability: Delving into fluency, accuracy and complexity. Research on Youth and Language, 7(2), 147-159.
- Gonen, M. (2009). The relationship between FL listening anxiety and FL listening strategies: The case of Turkish EFL learners. In Proceedings of the 5th WSEAS/ IASME International conference on educational technologies (pp. 44-49).
- Hariri, H., Karwan, D.H., Haenilah, E.Y., Rini, R., & Suparman, U. (2021). Motivation and learning strategies: Student motivation affects student learning strategies. European Journal of Educational Research, 10(1), 39-49.
- Hampden-Turner, C., & Trompenaars, F. (2006). Cultural intelligence: Is such a capacity credible? Group & Organization Management 31, 56-63.
- Hoe, S. L. (2008). Issues and procedures in adopting structural equation modeling technique. Journal of Applied Quantitative Methods, 3(1), 76-83.
- Horwitz, E. K., Horwitz, M. B., & Cope, J. (1986). Foreign language classroom anxiety. The Modern Language Journal, 70(2), 125–132 doi: 10.5840/ajs2012281-29
- Author et al. (2022). Epistemological beliefs and writing self-efficacy as predictors of second language writing anxiety: A structural equation modeling approach. Front. Psychol. 13, 850243. doi: 10.3389/fpsyg.2022.850243
- Hu, L., & P. Bentler, P. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives, Structural Equation Modeling. A Multidisciplinary Journal, 6(1), 1-55. https://doi.org/10.1080/10705519909540118
- Ji, S., Qin, X., & LI, K. (2022). A Systematic Review of Foreign Language Listening Anxiety: Focus on the Theoretical Definitions and Measurements. Front. Psychol. 13,859021. doi: 10.3389/fpsyg.2022.859021
- Kimura, H. (2008). Foreign language listening anxiety: Its dimensionality and group differences. JALT, 30(2), 173-196. https://doi.org/10.37546/JALTJJ30.2-2
- Kember, D. Leung, D. V. P., Jones, A., Loke, A. Y., McKay, J., Sinclair, K., Tse, H., Webb, C., Wong, M., & Yeung, E. (2000). Development of a questionnaire to measure the level of reflective thinking. Assessment and Evaluation in Higher Education, 25(4), 381-395. https://doi.org/10.1080/713611442
- Kim, J. H. (2000). Foreign language listening anxiety: A study of Korean students learning English. English Teaching, 57(2), 3-34.
- Liu, M. (2021). Foreign language classroom anxiety, gender, discipline, and English test performance: a cross-lagged regression study. Asia-Pacific Education Researcher, 1, 1–11. doi: 10.1007/s40299-020-00550-w
- Lambert Snodgrass, L., Ghahremani, M., & Hass, M. (2023). Live-learn-work: Experiential learning and cultural intelligence in the internship abroad. Journal of Global Education and Research, 7(1), 50-63. https://www.doi.org/10.5038/2577-509X.7.1.1120
- MacIntyre, P. D., & Gardner, R. C. (1994). The subtle effects of language anxiety on cognitive processing in the second language. Language learning, 44(2), 283-305
- MacIntyre, P. D., & Gardner, R. C. (1989). Anxiety and second-language learning: toward a theoretical clarification. Language Learning, 39, 251–275. https://doi.org/10.1111/j.1467-1770.1989.tb00423.x
- Murayama, K., & Elliot, A. J. (2019). Achievement goals. In R. M. Ryan (Ed.), The Oxford Handbook of Human Motivation (pp. 229–245). New York: Oxford University Press.
- Noels, K. A., Pelletier, L. G., Clément, R., & Vallerand, R. J. (2000). Why are you learning a second language? Motivational orientations and self-determination theory. Language Learning, 50, 57–85.
- Oteir, I. N., & Al-Otaibi, A. N. (2019). Foreign language anxiety: A systematic review. Arab World English Journal, 10, 309–317. doi: 10.24093/awej/vol10 no3.21
- Pekrun, R., Elliot, A. J., & Maier, M. A. (2009). Achievement goals and achievement emotions: Testing a model of their joint relations with academic performance,’ Journal of Educational Psychology, 101, 115–135. http://dx.doi.org/10.1037/a0013383
- Pintrich, P. R., & Schunk, D. H. (2002). Motivation in Education. Englewood Cliffs, NJ: Prentice Hall.
- Rotgans, J. I., & H. G. Schmidt., H. G. (2017). The relation between individual interest and knowledge acquisition. British Educational Research Journal, 43(2)350–71. https://doi.org/10.1002/berj.3268
- Ryan, R., & Deci. E. (2017). Self-Determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Publications
- Sternberg, Robert J., Vlad Glaveanu, Sareh Karami, James C. Kaufman, Shane N. Phillipson, and David D. Preiss. 2021b. Meta-intelligence: Understanding, control, and interactivity between creative, analytical, practical, and wisdom-based approaches in problem solving. Journal of Intelligence 9: 19. [Google Scholar] [CrossRef] [PubMed]
- Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed). Pearson: Allyn and Bacon.
- Teimouri, Y., Goetze, J., & Plonsky, L. (2019). Second language anxiety and achievement: A meta-analysis: Erratum. Studies in Second Language Acquisition, 41(2), 489-489. doi:10.1017/S0272263119000445
- Author et al (2020). The Interplay among EFL students' epistemic Beliefs, language learning strategies, and L2MSS: A SEM approach. JMRELS, 8(2), 169-195. doi.10.30479/jmrels. 2020.12621 1562.
- Spielberger, C. H. A. D. (1971). Development of the Spanish edition of the state-trait anxiety inventory1. Interamerican Journal of Psychology, 5(3-4), 145–158.
- Vandergrift, L. (2002). Second language listening: Listening ability or language proficiency? The Modern Language Journal, 90, 6–18. doi:10.1111/j.1540-4781.2006.00381.x
- Wheeless, L. R. (1975). An investigation of receiver apprehension and social context dimensions of communication apprehension. The Speech Teacher, 24, 261–268. https://doi.org/10.1080/03634527509378169
- Yang, B. (2020). The role of metacognition in cultural intelligence: A study of Chinese overseas students. Journal of International Students, 10(4), 1138-1154.
- Zimmerman, B. J. (1986). Becoming a self-regulated learner: Which are the key sub-processes? Contemporary Educational Psychology, 11, 307-313.
- Zimmerman, B. J., & Kitsantas, A. (2002). Acquiring writing revision and self-regulatory skill through observation and emulation. Journal of Educational Psychology, 94(4), 660–668. https://doi.org/10.1037/0022-0663.94.4.660
- Vandergrift, L. (1997). The comprehension strategies of second language (French) listeners: A descriptive study. Foreign Language Annals, 30(3), 387-409. DOI: 10.1111/j.1944-9720.1997.tb02362.x
- Vallerand, R. J. (1997). Toward a hierarchical model of intrinsic and extrinsic motivation. In M. P. Zanna (Ed.), Advances in experimental social psychology, Vol. 29, pp. 271–360). Academic Press. https://doi.org/10.1016/S0065-2601(08)60019-2
- Author et al (2022). Predicting of English language anxiety based on social learning strategy and language motivational self-system in students. SSYJ 13 (47), 121-132
No competing interests reported.