A Study of Chinese Undergraduate Students’ English Language Speaking Anxiety,Expectancy-Value Beliefs and Spoken English Proficiency

Abstract

Motivation and anxiety are two crucial factors influencing learning outcomes, yet limited empirical research on expectancy-value theory can be found within previous literature about Chinese undergraduate students studying English as a foreign language (EFL). Moreover, few studies have examined the interaction between motivation and skill-specific anxiety. Thus, the present study explored dimensions of task values of English learning, the relationship between expectancy, values and English language speaking anxiety (ELSA) among Chinese undergraduate EFL learners and their predictive power on spoken English proficiency. Two hundred twenty-three Chinese undergraduates completed a questionnaire about their spoken English proficiency, expectancy-value and ELSA items. The following results came to light: (1) task values in English learning had four facets; (2) different types of value were significantly positively correlated with each other, both expectancy and ELSA were significantly linked to cost value, and expectancy bore a significantly negative correlation with ELSA; (3) expectancy, ELSA and attainment and cost value separately predicted learning achievement, whereas only expectancy and value additively predicted learning achievement, where expectancy exerted a greater impact. These findings suggest that teachers should guide students to aim high and provide more opportunities for spoken English practice.

Plain Language Summary

Motivation and anxiety are two crucial individual factors influencing learning outcomes, and motivation can be subcategorized into expectancy and task values. Expectancy refers to one’s estimation of the probability to achieve an outcome, task values measures how much the individual values the desired outcome, and speaking anxiety points at the specific anxiety aroused by English speaking activities. In the present study, we conducted statistical analyses on survey results from 223 Chinese undergraduates. Findings indicated the link between expectancy, task values and English language speaking anxiety. Expectancy exerted greater influence on students’ spoken English proficiency than task values, and expectancy, values and English language speaking anxiety does not impact each other’s effect on spoken English proficiency. Based on the findings, we advised teachers to guide students to aim high and provide more opportunities for spoken English practice. Continuous research and firmer evidence are needed on students’ motivation, anxiety and L2 learning, and we identified the possibilities for research in this regard.

Keywords

expectancy value speaking anxiety spoken English proficiency Chinese undergraduates

Introduction

As two major psychological factors in second language acquisition, anxiety and motivation have been the focus of considerable research. Existing studies have approached motivation from different perspectives, including the instrumental-integrative or extrinsic-intrinsic dichotomy (Chambers, 1999; Deci & Ryan, 2000) and the classification of motivation by its usage (T.-Y. Kim, 2017; M. Liu, 2007), while many studies also focus on the expectancy-value theory (EVT) of language learning motivation (Barron & Hulleman, 2015; Conley, 2012; Nagle, 2021). Nevertheless, as important concepts and components of learning motivation, expectancy-value beliefs have not been adequately researched in English as a foreign language (EFL) learning (M. Liu & Dong, 2021; Y. Zhang, 2021).

Distinctions have been made between general second language classroom anxiety and language-skill-specific anxiety (Elkhafaifi, 2005; J. Kim, 2000), and among four skill-based language anxiety (Pae, 2013). Speaking anxiety is extremely baffling (Bademcioglu et al., 2017; Marzec-Stawiarska, 2015), and various factors have been identified for its emergence and its negative effect on English proficiency (Ansari, 2015; Marzec-Stawiarska, 2015; Sadighi & Dastpak, 2017). Several studies confirm the connections between FL motivation and anxiety (Alqahtani, 2018; Tahmouresi & Papi, 2021; X. Zhang et al., 2020). Yet, few studies consider the interaction between expectancy-value beliefs and anxiety in bilingual students when learning the second language for general purposes (M. Liu & Dong, 2021), despite previous evidence demonstrating the negative association between general motivation and anxiety (Bademcioglu et al., 2017; M. Liu & Huang, 2011) and expectancy and goal orientation’s direct impact on foreign language classroom anxiety (Cheng, 2001).

Controversies remain over the effect of motivation and anxiety on learning achievement (Bademcioglu et al., 2017; M. Liu & Huang, 2011), and structural equation modeling shows that both anxiety and motivation significantly predict language learning achievement, while some subcategories of motivation indirectly affect learning achievement (Alqahtani, 2018; Khodadady & Khajavy, 2013; J. Lee, 2020). Despite previous adoption of latent structural modeling to reduce the effect of measurement error (Meyer et al., 2019; Trautwein et al., 2012), it is surprising that no prior research has employed latent structural modeling equation to examine the predictive relationship of motivation and anxiety on Chinese university students’ English proficiency, to which EFL learning has attached great importance. Based on a large sample of undergraduate students in China, the present study attempted to fill these gaps by linking EVT and anxiety with latent interaction modeling.

Literature Review

EVT and Its Application

In studies of FL motivation, a range of theoretical frameworks have been drawn upon, including Gardner’s (2006) socio-educational model and Deci and Ryan’s (2004) self-determination theory, and identified significant positive relationships between FL motivation and achievement. Most of these studies explored the positive effect of motivation on learning proficiency (Tahmouresi & Papi, 2021; X. Zhang et al., 2020). Nevertheless, currently most FL motivation research has been conducted among university learners across cultural contexts outside of China, with the exception of several studies specifically addressing individual and classroom factors affecting students’ motivational fluctuations (Du & Jackson, 2018; J. Liu, 2022; Yu et al., 2019), the relationship between different types of motivation (Gao et al., 2014), and only one study dealing with the influence of motivation on students’ EFL proficiency (J. H. Lee & Lo, 2017).

Despite variance of different theoretical models of motivation in their breadth of focus on attitudes (Hood et al., 2012), our survey of the field reveals a dearth in the Expectancy-value theory of motivation (Eccles, 1983) research examining distinct motivational antecedents (i.e., expectancy and different task values), limiting the understanding of motivational constructs. Every decision regarding the choice of framework reflects its suitability for the sample, context, research question(s), methodology and/or preferences of the research team, to understand students’ motivation to learn a FL. In this study, Eccles (1983) expectancy-value theory of motivation was used as it fits contexts where students are engaging in compulsory English learning in Chinese universities. The distinction between expectancy and value is an interesting perspective to study (Hu & McGeown, 2020), as the Chinese higher education system places a high value on both academic success and the importance of learning English (Bolton & Graddol, 2012), which might be linked to expectancy and values of English learning.

The EVT, a theory of motivation, is characterized by its application in real achievement situations (Eccles & Wigfield, 2002; Guo, Marsh et al., 2015; Lauermann, Tsai, & Eccles, 2017). According to this theory, expectancy for success and task values directly influence performance, persistence, and task choice (Trautwein et al., 2012). Expectancy for success refers to one’s beliefs on their performance in a task, while task value measures one’s beliefs in their profitability to accomplish a task (Conley, 2012). Instruments of task values adopted in most empirical studies confirm the four constructs: attainment value, the personal importance of accomplishing a task; utility value, the usefulness of a task; intrinsic value, one’s enjoyment in a task; and cost value, one’s efforts in a task (Eccles, 1983; Eccles & Wigfield, 2002).

Until now, there is only scarce research on the construct of task values and the influence of expectancy and value on English learning outcomes among undergraduate students, despite two previous studies respectively revealing the effect of expectancy and value on undergraduates’ deep language learning strategies (Zhan et al., 2021) and primary school pupils’ learning achievement of English as a foreign language (Hu & McGeown, 2020). However, the separate influence of expectancy and different types of value is found in other fields, including math (Fan, 2011; Guo, Marsh et al., 2015; Musu-Gillette et al., 2015; Simpkins et al., 2006) and chemistry learning outcomes (Perez et al., 2014), intention for further study (Battle & Wigfield, 2003; Perez et al., 2014) and how and why students read (Perez et al., 2014). In particular, expectancy and value tend to respectively affect performance and interest, while cost, another subcomponent of value, predicts both (Barron & Hulleman, 2015). In line with the recent call to study expectancy and value in domain-specific areas (Guay & Bureau, 2018), there is a critical need to apply EVT in EFL research.

The interaction effect of expectancy and value, which does not play an important role in Eccles’s (1983) modern EVT model, has attracted researchers’ attention since the 2010s. Since the first inclusion of the multiplicative term into the modern EVT model (Nagengast et al., 2011) and the first calculation of its effect on learning achievement (Trautwein et al., 2012), a significant interaction effect has been found on learning achievement (Durik et al., 2006; Guo, Parker et al., 2015; J. Lee et al., 2013, 2014; Meyer et al., 2019; Trautwein et al., 2012) or achievement-related choices and behaviors (Guo, Marsh et al., 2015; Nagengast et al., 2013), with the adverse effect of high task value with low expectancy beliefs possibly caused by mental contrasting effects (Trautwein et al., 2012), whereas these studies on interaction effects are mostly restricted to work with high school EFL students. To date there has been one study of this interaction effect of undergraduate EFL students (Zhan et al., 2021), focusing specifically on the interaction effect on deep learning strategies.

In summary, this body of literature implies that, though there is abundant literature on English learning motivation and anxiety, the existing gap of current research is FL research within the framework of EVT and the simultaneous investigation of expectancy, value and speaking anxiety on spoken English proficiency, especially among Chinese undergraduate students. Hence, there is a critical need to examine these effects, especially in the undergraduate EFL context, as prior work focused on younger students.

Foreign Language Anxiety

General Foreign Language Anxiety

Anxiety is defined as apprehension caused by potential threats (Chastain, 1988). Previous research points out the necessity of putting anxiety in a certain context (MacIntyre & Gardner, 1991; Mischel & Peake, 1982). Such emotion also exists in SL/FL learning and is referred to as foreign language anxiety (Awan et al., 2010), and the classroom is defined as the source of foreign language anxiety (Horwitz et al., 1986).

Extensive research examines the impact of foreign language anxiety on learning achievement. In quantitative research, researchers employ several measures to examine anxiety from different perspectives and obtained generalized findings. For instance, Dewaele (2007a, 2007b) adopted an “anxometer”-like three-point Likert questionnaire dealing with the anxiety of learning different languages, while P. D. MacIntyre and Gardner (1994) developed scales to focus on different stages of foreign language learning. Besides, most researchers adopt revised versions of Horwitz et al.’s (1986) Foreign Language Classroom Anxiety Scale (FLCAS) (Hewitt & Stephenson, 2012; Marcos-Llinás & Garau, 2009; Sparks & Ganschow, 2007), and varying results are revealed by these studies. Some studies indicate that students’ foreign language learning anxiety was of a moderate level and it positively affected their learning achievement (Hewitt & Stephenson, 2012; M. Liu & Thondhlana, 2015; Park & French, 2013). Marcos-Llinás and Garau (2009) reported no significant predictive power of foreign classroom anxiety on college student’s performance, while most studies reveal a significantly negative effect of second language anxiety on learning achievement both among high school students (Dewaele, 2007a; Sparks & Ganschow, 2007) and among university students (Awan et al., 2010; Elkhafaifi, 2005; Hewitt & Stephenson, 2012; M. Liu & Thondhlana, 2015; M. Liu & Xiangming, 2019), which might be attributed to errors related to the target language (Gregersen, 2003), or unwillingness to speak this language (M. Liu & Jackson, 2008). Some recent studies adopt qualitative approaches to obtain more individualized and contextualized data in their exploration of the profile, the source and the impact of students’ foreign language anxiety (P. MacIntyre & Gregersen, 2012) across different cultural contexts, with examples of studies in Australia (Dryden et al., 2021), Hungary (Tóth, 2010), Iran (Elaniä Shiärvan & Talebzadeh, 2020) and the United States (Liao, 2021), among many others. These studies collected more individualized data from interviews, answers to open-ended questions, focus group and linguistic ethnography. Holistic and contextualized as they are, findings from qualitative approaches should be interpreted based on corresponding quantitative research (Abbuhl & Mackey, 2017). Current studies involving many aspects of learning generally adopt quantitative methods as these methods typically yield conclusions that can be generalized to a larger population (Abbuhl & Mackey, 2017).

Foreign Language Speaking Anxiety

Recent years have seen a movement of FLA research from general classroom anxiety to anxiety in four major skill areas: speaking, listening, reading and writing. For instance, while Chang (2008) identified the main sources of listening anxiety, X. X. Zhang (2013) detected the predictive effect of listening anxiety on FL listening performance. Capan and Karaca (2013) distinguished FL listening anxiety and reading anxiety and revealed a positive correlation between these two constructs. Gok et al. (2023) went on to clarify that online flipped classrooms did not significantly account for variances in reading anxiety. Qashoa (2014) discovered the effects and causes of foreign language (FL) writing anxiety. Compared with other skill-specific FL anxiety, foreign language speaking anxiety caught the attention of more researchers as foreign language classroom anxiety was primarily concerned with speaking, and foreign language speaking anxiety was linked to personality traits (Vural, 2019).

English language speaking anxiety is found to exert a negative influence on oral performances, resulting from peer pressure (Ansari, 2015; Marzec-Stawiarska, 2015) or grammatical errors corrected by negative feedback (Ahmed et al., 2017; Ansari, 2015; Dewaele, 2007a; Gregersen, 2003; Kitano, 2001; Marzec-Stawiarska, 2015; Sadighi & Dastpak, 2017). Linguistic difficulties other than grammar, for example, pronunciation and lexicon, might also contribute to increased anxiety (Ansari, 2015; Balemir, 2009; Debreli & Demirkan, 2015; Marzec-Stawiarska, 2015; Sadighi & Dastpak, 2017). Karatas et al. (2016) and Mahmoodzadeh (2012) respectively reported a negative effect and no effect of prior knowledge or training in the target language on foreign language speaking anxiety. Suggestions to alleviate students’ speaking anxiety include humanistic pedagogical approaches, tolerance for errors, a sense of humor (Ansari, 2015; He, 2017), and cooperative speaking activities (Ansari, 2015; Debreli & Demirkan, 2015; He, 2017; Sadighi & Dastpak, 2017). Most empirical studies reveal no significant correlations between English language speaking anxiety and language proficiency (Balemir, 2009; Çağatay, 2015; Karatas et al., 2016), except for a few studies suggesting the facilitative aspect of anxiety (Debreli & Demirkan, 2015; Kitano, 2001; Marcos-Llinás & Garau, 2009). Several meta-analysis research reveals a significant negative correlation between foreign language (FL) performance and FL oral anxiety or FL classroom anxiety that focused on speaking, and this correlation magnitude ranks only second to that between FL performance and FL listening anxiety (Botes et al., 2020; Teimouri et al., 2019; X. Zhang, 2019). Much less attention is given to the comparison between speaking anxiety and motivation in their influence on learning achievement, let alone the consideration of the EVT.

Rationales for the Present Study

As reviewed above, motivation and anxiety are two crucial factors that interact with each other and account for FL proficiency. Despite numerous studies attributing the variance in communicative anxiety to an increase in self-confidence and self-perceived ability which might be linked to expectancy (Ansari, 2015; Dewaele, 2007b; Enisa & Karairmak, 2017), more efforts are needed to tap into the simultaneous effects of expectancy, value and ELSA on FL proficiency. Latent structural modeling equations are well-suited to explore effects both separately and simultaneously. Despite the significance of establishing a connection between EVT and specific types of anxiety, the link between expectancy, values and anxiety-provoking speaking anxiety is still under investigation, and research on this link could further clarify the complex relationship between motivation and anxiety (Tóth, 2010). Compared with studies in other fields among high school students, parallel studies on undergraduate students’ English learning are rather scarce. Nevertheless, a clearer picture of the interaction between FL motivation, anxiety and English proficiency is of increasing significance in guiding university EFL teachers toward the application of more suitable pedagogy, due to post-secondary students’ greater involvement in English learning in recent years (Bolton & Graddol, 2012). Guided by the EVT, the investigation aimed to pursue the above objectives by examining:

the dimensions of the task values in English learning;

the relationship between expectancy, value and ELSA among Chinese undergraduate EFL learners; and

the predicting power of expectancy, value and ELSA on English proficiency.

Methodology

Participants

Two hundred twenty-three undergraduate participants, randomly selected from Tsinghua University, a top university in China, included an overrepresentation of male students (N = 149, 66.82%). With a mean age of 19.475 (SD = 1.335), these students were composed of 99 freshmen (44.39%), 55 sophomore (24.66%), 47 juniors (21.08%) and 22 seniors (9.87%) from four disciplines: arts (N = 45, 20.18%), sciences (N = 33, 14.80%), engineering (N = 116, 52.02%) and business (N = 29, 13.00%). All participating students were required to take a certain number of EFL course credits on graduation, and the number of required credits varied across disciplines. These Chinese native students exhibited great differences in their spoken English proficiency.

Instruments

The present study adopted a translated Chinese version of Y. Zhang’s (2021) battery of questionnaires. This inventory was composed of English Language Speaking Anxiety Scale, the Expectancy and Value Beliefs Scale and the background information questionnaire (see Appendix A). The former two scales were both placed on a 7-point Likert scale, ranging from “totally disagree” to “totally agree” with values 1 to 7 assigned to each of the descriptors respectively. In the Chinese undergraduate education system, the English as a foreign language (EFL) curriculum falls into the category of general education (Mohrman et al., 2012), aiming to increase students’ knowledge and broaden their horizon, hence the current study examined spoken English learning for general purposes, rather than academic purposes as in Y. Zhang’s (2021) study.

English Language Speaking Anxiety Scale (ELSAS)

This 12-item questionnaire was made by adapting items in the second language speaking anxiety scale (SLSAS) (Woodrow, 2006). Items in Woodrow’s (2006) SLSAS measure SL speaking anxiety in different communicative situations (e.g., in-class and out-of-class situations), and therefore it is suitable for studies that focus on undergraduates. As the present study examined the learning of spoken English for general purposes, items were adapted from those more related to academic English learning to those more related to a daily communicative context. For example, the item “I feel anxious when asked to present my research in English to someone I don’t know” was adapted to “I feel anxious when asked to make a self-introduction in English to someone I don’t know.”

Expectancy and Value Beliefs Scale (EVBS)

This questionnaire, with four items on expectancy and 12 items on value, originated from Trautwein et al.’s (2012) expectancy and value beliefs scales for students learning a specific subject. Some items were adapted to suit the daily communicative context of undergraduates. For example, expectancy items like “Communicating in English isn’t really my thing,” and value items like “I always look forward to practicing my oral English,” were added to the inventory and replaced the corresponding items of academic English. Similar to Trautwein et al.’s (2012) study, value items were divided into attainment value (three items, including “It is important to me personally to be good at English”), intrinsic value (five items, including “If I can practice speaking English, I’m prepared to use my free time to do so”), utility value (two items, e.g., “Fluent spoken English is important to me in the future”) and cost value (two items, e.g., “I’d have to invest a lot of time to get improvements in speaking English”). The outcome of item 13, “I feel difficult to understand everything related to English,” item 15, “I am just not good at using English” and item 16, “Communicating in English isn’t really my thing,” was recoded in a reverse way by subtracting the original number of these three items from 8, and the new number was the outcome of these three items.

The Background Information Questionnaire

This questionnaire collected students’ gender, age, year of study, major and self-rated spoken English proficiency with a 5-point scale: 1 being “very weak,” 2 being “weak,” 3 being “medium level,” 4 being “fairly good” and 5 being “excellent.” Participants reported their spoken English proficiency score out of 5 based on their self-appraisal, and this score provides a quantitative metric for students’ spoken English proficiency.

Data Collection and Analysis

The ELSAS, EVBS and the background information questionnaire were double-checked item by item before being administered to participants. All participants volunteered to answer this battery of questionnaires without financial reward. The materials were administered in Tsinghua University in March 2021.

This study applied the SPSSAU v.20.0 and Mplus 8.3 to conduct statistical analysis. First, the reliability of ELSA, expectancy and subscales of value was examined by calculation of Cronbach α values. Second, confirmatory factor analysis (CFA) was applied to examine components of value. Third, fit measures of the structural equation were calculated, including root mean square error of approximation (RMSEA), χ² test statistic and comparative fit index (CFI), the standard root mean square residual (SRMR) and the correlation between expectancy, value and ELSA. Fourth, the separate predicting power of expectancy, value and speaking anxiety, and the simultaneous predicting power of both expectancy and values on learning achievement were investigated with structural equation modeling (SEM). All the above steps were conducted in SPSSAU.

Finally, with the latent modeling structure (LMS) model in Mplus 8.3, we explored whether the predictive power of the interaction term is significant or not. When assessing the impact of expectancy, values and ELSA on learning outcomes, SEM with latent variables in this study might help eliminate the measurement errors since all these variables were measured by several scales. Large sample sizes and highly reliable predictor variables help to avoid Type 2 errors (i.e., not finding evidence for a statistically significant interaction although one exists), and structural equation modeling techniques that control for measurement error by modeling latent variables with multiple indicators are a potential solution for the reliability problem in multiple regression analyses, while conventional structural equation modeling and path analyses cannot account for interactions and other nonlinear effects of latent variables (Meyer et al., 2019; Musil et al., 1998; Trautwein et al., 2012). Prior works lend support to the importance of employing latent modeling equations in research concerning EVT in learning to examine the interaction effect, since indicators were treated as continuous variables (Hensley, 2014; Nagengast et al., 2013).

Results and Discussion

Multidimensionality of Value Beliefs

Good internal reliability was shown among items 1 to 12 (α = .928), items 13 to 16 (α = .825), items 20 to 24 (α = .822), items 25 to 26 (α = .896) and items 27 to 28 (α = .895), demonstrating good consistency of expectancy, ELSA, intrinsic, utility and cost value. However, subscales of attainment value (item 17–19, α = .252) were not consistent. Item 17 was eliminated and the two remaining components of this subscale, items 18 and 19, showed good internal reliability (α = .902).

CFA tested the four-dimension structure of task value (Meyer et al., 2019; Nagengast et al., 2011, 2013; Trautwein et al., 2012). Four latent factors representing the components of task value were specified, and each item was allowed to load on only one factor, not allowing residual correlations. Brown (2015) indicated that the CFI greater than 0.9, the RMSEA close to or lower than 0.08 and the SRMR close to or lower than 0.08 suggested an acceptable good fit. Therefore, the acceptable model fit of this structure supported the four-factor structure of value: χ²(398, N = 233) =661.893, p < .001, CFI = 0.906, RMSEA = 0.072, SRMR = 0.071, with factor loadings in Appendix B.

The Relationship Between Expectancy, Value and ELSA

The Relationship Between Different Value Components

Among all significant correlations among the latent factors of the task value components (Table 1), the highest was found between attainment value and utility value (r = .861, p < .001), and the lowest between intrinsic value and cost value (r = .173, p < .05). This finding was inconsistent with Trautwein et al.’s (2012) finding that the highest correlation was between attainment value and intrinsic value, and the lowest between utility value and cost value. It is possible that, compared with high school students, college students’ sense of attainment mostly arises from career-oriented usage of English. According to Durik et al. (2006), the high correlation between attainment value and utility value could result from the role of the usefulness of a task in promoting one’s competence performance and gearing one toward a more ideal self-concept. The correlations between cost value and other value components were positive instead of negative, contrary to Barron and Hulleman’s (2015) conclusion. Most inter-correlations of the four latent factors did not exceed r = .9, corroborating the multi-dimensionality of value (Barron & Hulleman, 2015; Durik et al., 2006; Meyer et al., 2019; Trautwein et al., 2012).

Table 1.

Correlations Between Task Value Subscales, Expectancies and ELSA.

	Attainment value	Intrinsic value	Utility value	Cost value	Expectancies
Attainment value
Intrinsic value	0.626** (0.000)
Utility value	0.861** (0.000)	0.639** (0.000)
Cost value	0.270** (0.000)	0.173* (0.026)	0.293** (0.000)
Expectancy	0.086 (0.261)	0.090 (0.254)	0.091 (0.235)	−0.585** (0.000)
ELSA	−0.080 (0.274)	−0.084 (0.266)	−0.117 (0.112)	0.532** (0.000)	−0.758** (0.000)

p < .05. **p < .001.

The Relationship Between Expectancy and Different Subscales of Value

Unlike the strong correlation found among high school students (Hood et al., 2012; Trautwein et al., 2012), this study discovered no significant correlation between expectancy and different subscales of value (Table 1), despite the significant high negative correlation between expectancy and cost value (r = −.585, p < .001) (Table 1). Changes in relations between expectancy and value over time (Wigfield et al., 2020) might be attributed to students’ more accurate self-understanding and less frequently relating value to expectancy when ascending from secondary schools to universities (Loh, 2019).

The Relationship Between Expectancy or Different Value Components and Elsa

A high negative correlation between expectancy and ELSA (see Table 1, r = − 0.798, p < .001) in this study seemed to support Cheng’s (2001) conclusion that expectancy directly contributes to foreign language classroom anxiety with the indirect contribution of giftedness beliefs, and Hood et al.’s (2012) indication of expectancy’s strong correlations with affection. Regarding Yamashita’s (2013) findings that extensive reading increased comfort value and alleviated anxiety, it could be speculated that the high correlation between cost value and ELSA (see Table 1, r = .532, p < .001) in the present study might be related to the exposure to English environment.

However, the correlation was not significant between speaking anxiety and attainment, intrinsic and utility value (see Table 1). The findings were not in line with Alqahtani’s (2018) findings that English learning anxiety is related to three aspects of second language (L2) motivation: the ideal L2 self, the ought-to-L2 self and the language learning attitude. The ideal L2 self, which reflects students’ hopes and aspirations, corresponds to attainment value that places great importance on high task performance (Durik et al., 2006; Nagle, 2021); ought-to-L2 self, which is less internalized in nature, is similar to utility value; and language learning attitude which is more internally or intrinsically rooted (Alqahtani, 2018). Moreover, the result was inconsistent with the entity theory of intelligence that goal orientation directly influences foreign language classroom anxiety (Cheng, 2001), where goal orientation is manifested as value in this study, since goal orientation refers to the reason for approaching a task (Hsieh et al., 2007). The lack of significant correlation between these variables might be due to the interference of cognitive competence whose mediating effect was found on the relationship between anxiety and value (Hood et al., 2012).

A prudent conclusion from the above findings is that ELSA is more expectancy- rather than value-driven compared with general anxiety, possibly because foreign language speaking anxiety arises from communication with the instructor, grammatical errors and peer pressure (Ahmed et al., 2017; Ansari, 2015; Dewaele, 2007b; Gregersen, 2003; Kitano, 2001; Marzec-Stawiarska, 2015; Sadighi & Dastpak, 2017), which are closely related to students’ expectation for their oral performance.

The Predicting Power of Expectancy, Value and ELSA on English Proficiency

Expectancy, Value and ELSA as Separate Predictors of English Proficiency

Self-rated spoken English proficiency was found to be significantly correlated with gender (r = .182, p < .01), age (r = .154, p < .05) and year of study (r = .169, p < .05), but not with discipline (Table 2). Therefore, the present study set gender, age and year of study as control variables.

Table 2.

Correlations Between Self-Rated Spoken English Proficiency and Gender, Age, Discipline and Year of Study.

	Self-rated spoken English proficiency
Gender	0.182** (0.007)
Age	0.154* (0.022)
Discipline	0.069 (0.304)
Year of study	0.169* (0.012)

p < .05. **p < .01.

To examine the separate or simultaneous predicting power of expectancy, value and ELSA over spoken English proficiency, the results of a series of structural equation regression models (Meyer et al., 2019; Trautwein et al., 2012) were reported in Table 3. A baseline model was firstly run with three control variables (gender, age and year of study as control variables), in which the residual variance reached 0.939 and therefore 6.1% of the variance in English proficiency was explained by the control variables.

Table 3.

Predicting Spoken English Proficiency: Results From Latent Structural Equation Modeling With Only One Predictor.

Predictor	Exp	ELSA	AV	IV	UV	CV
β_{predictor variable} (t-value)	0.671*** (0.000)	−0.567*** (0.000)	0.142* (0.036)	0.104 (0.145)	0.126 (0.069)	−0.385*** (0.000)
β_gender (t-value)	0.123* (0.016)	0.191*** (0.000)	0.165* (0.011)	0.165* (0.013)	0.154 (0.021)	0.172** (0.004)
β_age (t-value)	0.030 (0.736)	0.038 (0.679)	0.065 (0.555)	0.074 (0.505)	0.057 (0.606)	0.000 (0.998)
β_{year of study} (t-value)	0.036 (0.684)	0.109 (0.236)	0.111 (0.317)	0.113 (0.311)	0.114 (0.306)	0.169 (0.105)
Residual variance	0.501	0.616	0.917	0.926	0.922	0.790
Model fit
χ²	811.623	842.184	923.017	925.388	924.185	894.133
Df	398	398	398	398	398	398
CFI	0.901	0.894	0.874	0.874	0.874	0.881
RMSEA	0.068	0.071	0.077	0.077	0.077	0.075
SRMR	0.067	0.068	0.102	0.101	0.101	0.085

Note. All multi-indicator constructs were modeled as latent variables. Exp = expectancy; ELSA = English language speaking anxiety; AV = attainment value; IV = intrinsic value; UV = utility value; CV = cost value.

*p < .05. **p <.01. ***p <.001.

Next, spoken English proficiency was regressed merely on expectancy and control variables rather than ELSA and value. In this model, both expectancy (b = 0.671, p < .001) and gender (b = 0.123, p < .05) significantly predicted spoken English proficiency with the residual variance decreasing to 0.501, whereas age and year of study did not. The significant predictive effects of two variables demonstrated that students with higher expectancy were more proficient in spoken English, as is shown in previous studies on elementary or high school students (Hu & McGeown, 2020; Meyer et al., 2019; Trautwein et al., 2012), and female students were greater in their spoken English proficiency than male students.

In a new model with only ELSA and control variables entered, the residual variance dropped to 0.66 and both ELSA (b = −0.567, p < .001) and gender (b = 0.191, p < .001) significantly predicted spoken English proficiency, consistent with most previous studies (Ansari, 2015; Dewaele, 2007a; Hewitt & Stephenson, 2012; Kitano, 2001; Woodrow, 2006) except for results in a few studies with participants not distributed across various academic backgrounds (Balemir, 2009; Çağatay, 2015; Marcos-Llinás & Garau, 2009).

In the next four models, attainment, intrinsic, utility and cost values respectively entered as separate predictors, with four control variables unchanged, and only attainment value (b = 0.142, p < .05) and cost value (b = −0.385, p < .001) were found as significant predictors of spoken English proficiency among four components of values. This could be attributed to the close relationship between attainment value and one’s language proficiency, and cost value’s hindrance of busy college students from spending time studying languages (Barron & Hulleman, 2015). This finding departs from that of previous studies (Meyer et al., 2019; Spinath et al., 2006; Trautwein et al., 2012) that revealed the significant predicting power of all four value components on learning achievement. College students learn in a more career-oriented and time-saving way than secondary school students, hence intrinsic motivation gives way to a growth mindset in its effect on self-regulated learning (Bai & Wang, 2023). Besides, no significant predicting power of utility value was found in the present study, incongruent with previous research (Durik et al., 2015; Guo, Marsh et al., 2015; Meyer et al., 2019; Trautwein et al., 2012; Üner et al., 2020), since utility value, significantly predictive of later learning interest (Fryer & Ainley, 2019), might be closely related with intrinsic value. Hence, utility value displays the lack of significant effect on learning achievement as intrinsic value. However, the significant predicting power of attainment value and cost value in this study echoes with previous findings (Barron & Hulleman, 2015; Meyer et al., 2019; Trautwein et al., 2012). The linear regression model’s residual variance, namely 0.917 for the model with attainment value and 0.790 for the model with cost value, was slightly smaller than the baseline model. Therefore, expectancy, ELSA, attainment value and cost value separately predict Chinese undergraduates’ spoken English proficiency.

Expectancy and ELSA as Additive Predictors of Spoken English Proficiency

The simultaneous predicting power of expectancy, value and ELSA on spoken English learning outcomes is shown in Table 4. M01 revealed that expectancy (b = 0.572, p < .001) was a more powerful predictor of spoken English proficiency than ELSA, while ELSA did not bear a significant coefficient in this model. The residual variance of M01 was slightly larger than those of the regression model with a separate expectancy variable, showing that M01 is a model less fitted to real situations. This does not agree with M. Liu and Huang (2011) who found that motivation and anxiety jointly significantly predict learning outcomes. One possible explanation might be the lack of consideration of expectancy and value’s synergistic effect on speaking anxiety (Lauermann, Eccles, & Pekrun, 2017). Besides, incongruence with Tanaka et al.’s (2006) discovery that anxiety rather than expectancy significantly predicts presentation performance might result from the differences between general speaking and classroom presentation. In general speaking situations, expectancy might have a larger effect on learning achievement than ELSA, and future research should tap into the interaction effect of expectancy and value on ELSA.

Table 4.

Predicting Spoken English Proficiency: Results From Latent Structural Equation Modeling With More Than One Predictor.

Predictors	M01: Exp and ELSA	M02: Exp and AV	M03: Exp, AV and Int (Exp×AV)	M04: Exp and CV	M05: Exp, CV and Int (Exp×CV)
β_exp (t-value)	0.572*** (0.000)	0.663*** (0.000)	0.754*** (0.000)	0.717*** (0.000)	0.808*** (0.000)
β_ELSA (t-value)	−0.116 (0.216)	-	-	-	-
β_values (t-value)	-	0.093 (0.082)	0.094 (0.210)	0.069 (0.329)	0.030 (0.582)
β_int (t-value)	-	-	0.060 (0.485)	-	−0.024 (0.630)
β_gender (t-value)	0.136** (0.008)	0.112* (0.030)	0.272* (0.013)	0.121* (0.019)	0.304** (0.006)
β_age (t-value)	0.031 (0.716)	0.020 (0.815)	0.027 (0.742)	0.040 (0.651)	0.020 (0.807)
β_{year of study} (t-value)	0.046 (0.595)	0.045 (0.604)	0.036 (0.736)	0.019 (0.834)	0.056 (0.598)
Residual variance	0.508	0.495	0.532	0.496	0.532
Model fit
χ²	799.663	808.636	N/A	810.696	N/A
Df	397	397		397
CFI	0.904	0.901	N/A	0.901	N/A
RMSEA	0.067	0.068	N/A	0.069	N/A
SRMR	0.067	0.067	N/A	0.067	N/A

Note. All multi-indicator constructs were modeled as latent variables. Traditional fit indices are not available for models with latent product terms. Exp = expectancy; Int = expectancy × value.

p < .05. **p < .01. ***p < .001.

Expectancy and Value as Additive Predictors of Spoken English Proficiency

With no significant predicting power of intrinsic or utility value revealed on spoken English proficiency, only the effect of attainment and cost value was considered when examining the simultaneous effect of expectancy and value. M02 and M04 respectively displayed greater predicting power of expectancy than that of attainment and cost value, and the coefficient of attainment or cost value was no longer significant due to the close relationship between expectancy and spoken English proficiency, similar to previous findings (Meyer et al., 2019; Trautwein et al., 2012) showing changes in the significance of the coefficients of all four types of values. However, the results contradict two previous studies (Guo, Parker et al., 2015; J. Lee et al., 2013) that revealed the significant predicting power of value when they were entered into a regression model of achievement together with expectancy, probably due to differences in students’ age. The model that fit statistics of M02 and M04 were more acceptable than that of the regression model with only expectancy or value variable, and the residual variances of M02 (0.495), as well as M04 (0.496), were smaller than their counterparts with only separate variables (0.901 for the model with expectancy variable, 0.917 for the model with attainment value, and 0.790 for the model with cost value), showing that simultaneous effects of expectancy and value provided a better explanation of real situations. The comparison of the present model with the baseline model revealed that expectancy accounted for 43.1% of variances, attainment value 0.6% and cost value 0.5%.

Expectancy and Value as Synergistic Predictors of Spoken English Proficiency

Finally, two models M03 and M05 included both expectancy and value and their interaction, with M03 concerning attainment value and M05 concerning cost value. Inconsistent with recent findings (Fong & Kremer, 2020; Guo, Marsh et al., 2015; Lauermann, Tsai, & Eccles, 2017) indicating significant interaction effect mostly among students in middle school, the present study discovered that both task values and the product term expectancy × value did not significantly predict spoken English proficiency. The residual variance became slightly larger in M03 (0.532) than in M02 (0.495), and in M05 (0.532) than in M04 (0.496), demonstrating a decrease in the goodness of fit with the interaction included, for the interaction term largely determines learning aspiration or goal commitment (Guo et al., 2017; Shah & Higgins, 1997). A plausible explanation might be that higher aspiration leads to greater learning outcomes in high school (Abu-Hilal, 2000; Christofides et al., 2015; Le et al., 2019), while for EFL undergraduates, students’ learning aspiration is closely related to CET-4 test outcomes instead of spoken English proficiency (Zhou, 2015). Learning aspiration in university largely depends on whether things learned in university are rewarded by the labor market or not (Robertson, 1995) and CET-4 test score is a more convincing indicator of one’s English proficiency than the spoken English score in most workplace cases in China. Therefore, Chinese university students are not compelled to practice spoken English for their careers, despite their great English learning aspirations.

To sum up, expectancy is a greater predictor of achievement outcomes than value, as is supported by some previous researchers (Barron & Hulleman, 2015; Guo, Marsh et al., 2015), and expectancy has greater predicting power than ELSA as well. There is no support for the significant role the interaction term plays in predicting spoken English proficiency.

Conclusion

Main Findings

This study constituted an important advance from prior research by investigating the correlation between motivation and anxiety and both the separate and the simultaneous impact of these variables upon learning achievement among undergraduate instead of younger EFL learners. The current study analyzed motivation within the framework of the expectancy-value theory and examined speaking anxiety rather than general anxiety, allowing a more accurate and specific examination of the relationship and the effect. It extended prior research by revealing the following correlations: (a) the positive correlation among different types of value, with the highest between attainment value and utility value yet the lowest between intrinsic value and cost value; (b) the positive correlation between cost value and expectancy; (c) the negative correlation between cost value and ELSA; and (d) the negative correlation between expectancy and ELSA. Besides, the latent structural modeling equation analyses indicated: (a) the separate and significant predicting power of expectancy, ELSA, attainment value and cost value on spoken English proficiency, with gender being a significant control variable; (b) the additive rather than the synergistic effect of expectancy and value on learning achievement. Furthermore, the present investigation found the one-dimensionality of expectancy and ELSA and corroborated the four-dimension structure of value.

Implications

The present study and that of Hood et al. (2012) identified that ELSA might be expectancy- rather than value-driven and that expectancy had a high predicting power on spoken English proficiency, which suggests the importance of promoting students’ understanding of their competency and a rather high expectation for their performance. Given this finding, it would be desirable for teachers to provide more positive feedback (Alavi & Kaivanpanah, 2007) and set more positive examples.

Additionally, the current findings regarding value suggested that value exerted an effect, though not so great as that of expectancy, on spoken English proficiency as well. Since the interaction term of expectancy and value is closely linked to deep language learning strategy (Zhan et al., 2021), the effect of value should not be neglected. These findings added to the desirability of promoting students’ understanding of what they can achieve through speaking English well by recommending students who do well in oral interpretation to attend international conferences, or increasing students’ opportunities to contact native speakers.

Limitations and Suggestions for Future Research

Despite these insightful findings, certain limitations existed within the present study. First, a more careful look should be taken at the magnitude of the effect of different factors on learning achievement, as the high inter-correlation between expectancy, value and anxiety in the present study preclude any firm conclusion. Given the outcomes provided by CFA, the focus of the present study was on four-faceted expectancy-value inventory and speaking anxiety instead of further dividing these scales, while recent research proposed the division of cost value into three components (Part et al., 2020). The significant correlation between cost value and other components in the present study also suggested the division of existing value components in future studies, and the mediating role of some factors affecting both motivation and anxiety should be examined as well, including strategies and attitudes (Bademcioglu et al., 2017).

Moreover, the present study collected data in a cross-sectional way and the hypothesized effect direction was from motivational and affective variables on learning achievement. Nevertheless, some researchers revealed the effect of prior achievement on expectancy-value beliefs and emotional management (Durik et al., 2006; Xu et al., 2019). Future research is advised to consider the longitudinal effect of expectancy, value and ELSA on learning achievement, and the bi-directional effect between them.

Another limitation was that the participants in this study came from only one university. Given that few previous studies tapped into the expectancy-value inventory and speaking anxiety in the meantime, the elimination of the school effect is needed in future studies on the separate and simultaneous effect of expectancy, value and anxiety on learning achievement. Future research is advised to involve samples from other universities to tap into the generalizability of the present findings, and possible causes should be investigated for the gap between results concerning high school and college students.

Footnotes

Appendix A Questionnaire

Gender ___________Age___________Discipline___________Year of study

Self-rated Spoken English Proficiency: □Very Weak □ Weak □Medium Level □Fairly Good □Excellent

Below: 1 = Totally Disagree; 2 = Strongly Disagree; 3 = Disagree; 4 = Uncertain; 5 = Agree; 6 = Strongly Agree; 7 = Totally Agree

Appendix B Factor Loadings

Factors	Items	Coef.	Std.Error	z (CR value)	P	Std. Estimate
Attainment value	q18	1.000	-	-	-	0.882
	q19	1.044	0.056	18.780	.000	0.931
Intrinsic value	q20	1.000	-	-	-	0.751
	q21	0.752	0.092	8.150	.000	0.576
	q22	0.939	0.094	9.971	.000	0.700
	q23	1.073	0.091	11.786	.000	0.842
	q24	0.803	0.096	8.335	.000	0.589
Utility value	q25	1.000	-	-	-	0.921
	q26	0.884	0.047	18.649	.000	0.884
Cost value	q27	1.000	-	-	-	0.935
	q28	0.913	0.062	14.607	.000	0.867
Expectancies	q13new	1.000	-	-	-	0.563
	q14	1.058	0.138	7.691	.000	0.686
	q15new	1.490	0.173	8.618	.000	0.840
	q16new	1.483	0.171	8.683	.000	0.855
Speaking anxiety	q1	1.000	-	-	-	0.720
	q2	1.043	0.098	10.603	.000	0.723
	q3	1.102	0.096	11.536	.000	0.785
	q4	0.961	0.101	9.511	.000	0.650
	q5	1.037	0.092	11.225	.000	0.764
	q6	0.859	0.096	8.915	.000	0.610
	q7	1.151	0.095	12.051	.000	0.819
	q8	1.037	0.099	10.487	.000	0.715
	q9	0.989	0.095	10.387	.000	0.709
	q10	1.085	0.097	11.205	.000	0.763
	q11	1.047	0.103	10.188	.000	0.695
	q12	1.008	0.097	10.397	.000	0.709

Acknowledgements

Words cannot express my gratitude to my honorable supervisor Prof. Meihua Liu. She kept on guiding me when I was perplexed about the topic to discuss in this paper and how to conduct quantitative analysis in face of difficulties in paper drafting, and propelling me to take rapid action once thinking of writing on a topic and explore an unknown field through self-study.

I would also like to express my appreciation for Prof. Yining Zhang and Prof. Qian Guo who offered insightful comments and advice during my writing. These precious comments were important guidelines for revision of this paper.

Lastly, I’d like to thank Lianqi Dong, Mingzhu Li, Jenna Wichterman and Celina Yeh Jiang for comments that greatly improved the manuscript, and I am grateful to 3 reviewers for their insights.

Author Contributions

Zhangwei Chen. Her research interests include the learning of English as a foreign language (EFL) and educational psychology in second language acquisition.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Zhangwei Chen

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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