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Abstract

Students’ perceived teacher support (SPTS) is considered crucial to students’ learning. But most existing SPTS scales were developed for the field of general education, and there is a lack of studies that attempt to develop a tailored scale specifically for the context of EFL learning. This study interviewed 86 Chinese college EFL learners and surveyed 1,089 Chinese non-English majors to develop an SPTS scale specifically for the context of college EFL learning in China. Exploratory and confirmatory factor analyses were conducted. The result indicated that SPTS is a tripartite construct consisting of emotional, intellectual, and social support. This study contributes to a better understanding of the unique aspects of language teaching and emphasizes the significance of tailored SPTS scales in different educational settings. The study elucidates the importance of SPTS in college EFL learning, providing guidance for effective teaching practices to enhance students’ learning experience.

Plain Language Summary

How college students perceive teacher support in English classes: developing and validating a new scale

This study focuses on understanding how college students learning English as a foreign language perceive support from their teachers. Recognizing that existing scales were mostly designed for general education, the researchers aimed to create a tailored scale for the specific context of English learning in China. Through interviews and surveys with Chinese college students, they identified three main types of support: emotional (care and encouragement), intellectual (guidance on learning strategies), and social (promoting interactions). The study highlights the importance of these supports in enhancing students’ learning experiences and provides a reliable tool for measuring teacher support in English language education.

Keywords

students’ perceived teacher support emotional support intellectual support social support

Introduction

In foreign language classrooms, teachers are the chief creators of students’ language learning environment (Klem & Connell, 2004) and also the most proximal factor that affects students (Schunk & Mullen, 2012). A body of research in the field of general education proved that students’ perceived teacher support (SPTS) is significantly related to learners’ self-efficacy (Bandura & Schunk, 1981), learning burnout (Romano et al., 2020), dropout (Wang & Eccles, 2012), well-being (Hagenauer et al., 2015), willingness to communicate (Zarrinabadi, 2014), student engagement (Roorda et al., 2011) and academic achievement (Wentzel, 1998). Likewise, a few researchers in the field of foreign language education started to pay attention to SPTS (Jin & Dewaele, 2018; H. Liu & Li, 2023; H. Liu et al., 2023). Nevertheless, the existing literature on SPTS is still relatively scanty and fragmented. Specifically, most existing SPTS scales were developed for the field of general education, and it is still unclear whether the concept of SPTS in general education is equivalent to that of English as foreign language learning (EFL) setting. Additionally, many studies have overlooked the fact that SPTS may differ when conducting studies in different cultural settings and different countries (Bai et al., 2019; Chen et al., 2018; Cui et al., 2017; Jia et al., 2020; Ouyang, 2005; Yu, 2019; Zhu & Wu, 2010). On top of that, applicability problems might occur when using a borrowed or adapted scale to measure participants from different discipline domains or different age groups (Chai & Gong, 2013; Chen, 2008; Gao et al., 2023; Hejazi et al., 2023; Liu et al., 2016; Spilt et al., 2012; Wu et al., 2023).

To bridge the gaps in current research, this study attempts to explore and verify the internal structure of SPTS within the context of college EFL learning, so as to construct a reliable and valid scale for measuring SPTS. McArthur and Baron (1983) posited that a lack of understanding about students’ perceptual information hinders our comprehension of their cognition. By shedding light on the psychological changes and cognitive processes of learners during the English learning process, the present study can offer guidance for English teachers to enhance their teaching practice with targeted intervention behavior, and in turn, improve students’ learning experience.

Literature Review

SPTS refers to students’ perceived teachers’ care, understanding, love, encouragement, respect, and assistance for students’ learning and emotional well-being (Liu & Li, 2023; Trickett & Moos, 1973; Yeung & Leadbeater, 2010). Teachers play a direct role in changing the classroom environment, adjusting teaching or interpersonal communication strategies, strengthening students’ learning objectives, and shaping students’ efficacy beliefs (Rui & Liu, 2023; Schunk & Mullen, 2012). Besides, SPTS is found to predict L2-grit (Derakhshan et al., 2023; Liu et al., 2023; Wu et al., 2023), psychological well-being (Gao et al., 2023), willingness to communicate (Hejazi et al., 2023), engagement (Yang & Du, 2023; Zhou et al., 2023) and students’ academic performance (Bai et al., 2019; Wentzel, 1998). It is also deemed that SPTS, compared with the support of parents and peers, can better explain students’ academic satisfaction and academic achievement changes (Plunkett et al., 2008).

Generally, the current interests in SPTS are underpinned by self-determination theory (SDT; Deci & Ryan, 2000, 2013). SDT indicates that individuals universally possess psychological needs for competence and relatedness, and it also suggests that the social environment, as a vital external factor, can support or hinder the satisfaction of an individual’s basic psychological needs, thereby affecting an individual’s behavioral motivation. According to Niemiec and Ryan (2009), students’ relatedness portrays the need to feel a sense of belonging and attachment to others, while students’ competence denotes the need to gain mastery of tasks and learn different skills, which can be supported by educators’ introducing various activities, appropriate tools, feedback, and learning strategies. In the educational context, Niemiec and Ryan (2009) also suggest that when students’ needs for competence and relatedness are supported, they are more likely to internalize learning motivation and engage more autonomously in learning. Hence, to foster the shift of students’ extrinsic motivation towards more integrated forms, such as intrinsic motivation (Deci & Ryan, 2013), it’s crucial for teachers to create a supportive learning environment to meet students’ psychological needs, which in turn can stimulate students’ enthusiasm and desire of learning.

Teacher support is essential for successful learning (Liu et al., 2023), and if this support is perceived by students (students’ perceived teacher support), it can positively predict school and classroom-related learning interests (Wentzel, 1998), L2 grit (Derakhshan et al., 2023), willingness to communicate (Hejazi et al., 2023), and has a significant impact on academic outcomes (Bai et al., 2019; Wentzel et al., 2016).

SPTS can be viewed either from a single-dimensional or multidimensional lens (Brewster & Bowen, 2004; Chen, 2005; Malecki & Demaray, 2003; Shelton, 2003). Studies employing a multidimensional view divided SPTS into nuanced dimensions. Some researchers regard SPTS as a three-dimensional construct; for instance, as defined in Chen’s (2005) study, SPTS involves emotional (e.g., encouragement), instrumental (e.g., homework assistance, providing educational resources), and cognitive support (e.g., communicating the value of educational success). Ouyang (2005) characterized SPTS as intellectual (e.g., answering questions), emotional (e.g., providing encouragement), and ability support (e.g., participating in competitions and activities). Chai and Gong (2013) developed a model of SPTS for mathematics, consisting of three dimensions which respectively are autonomous, cognitive, and emotional support. In a recent study, Liu and Li (2023) successfully identified and validated a tri-factorial structure of the SPTS model, which encompasses academic, instrumental, and emotional support within the context of high school EFL learning in China.

Other studies considered SPTS a four-dimensional variable. In Malecki and Demaray (2003), emotional, instrumental, informational, and appraisal support were used to examine SPTS. Jiang et al. (2018) established a SPTS model within the context of online teaching, which is comprised of emotional support, social support, intellectual support, and instrumental support. Emotional support refers to teachers creating a positive learning atmosphere, valuing students’ thoughts, and being sensitive to their emotional needs. Social support refers to teachers engaging in learner activities, promoting communication between learners, and encouraging interactions among students. Intellectual support involves teachers using vivid and fascinating language to optimally meet teaching and students’ needs. Instrumental support refers to teachers providing technical knowledge and teaching information to support students.

When introducing a scale from abroad for local research, challenges of acclimatization are inevitable due to differences in language, cultural background, and educational methods (Liu et al., 2020). This raises the question of whether the SPTS varies across different countries and cultural settings. In addition, existing studies have primarily examined SPTS in general education. However, whether the concept of SPTS in general education aligns with that in EFL settings remains under-explored, leaving the applicability of SPTS in foreign language learning situations uncertain. Several reasons can elucidate why teacher support scales in general education cannot be directly applied to the EFL context. Firstly, different from learners of other subjects, EFL learners may face various challenges posed by lexical, grammatical and pronunciational demands. Secondly, EFL learning usually takes place in non-English-speaking countries or regions, where learners may have limited opportunities to be exposed to and use English in their daily lives. Therefore, in language classrooms, learners are always encouraged to interact with each other in English to get more language learning opportunities (Long & Xu, 2023). The frequent use of spoken language imposes extra anxiety on language learners (Bacon & Finnemann, 1990), therefore, targeted teacher support might help alleviate negative EFL learning emotions.

Furthermore, students may demonstrate different learning behaviors and perceptions of teacher support across various discipline domains and age groups. Research indicated that students’ school participation decreases as they progress from primary to middle to high school (Marks, 2000), suggesting that learners at different stages may have distinct learning behaviors. Unfortunately, a body of research in China adapted or directly borrowed exotic SPTS scales for general education research without differentiating the differences in participants’ cultural backgrounds. Cui et al. (2017), using SPTSQ (Students’ Perception to Teachers’ Supporting Questionnaire) developed by Babad (1990) and revised by Ouyang (2005), investigated left-at-home rural lower secondary school students’ characteristics of academic adaptation, and how their personality mediates between the perceived teacher support and their academic adaptation. Jia et al. (2020), utilizing SPTSQ adapted by Ouyang (2005), conducted a survey on high school students exploring the effect of perceived teacher support on their academic engagement. Yu (2019), also using SPTSQ, scrutinized the relationship between teacher support and students’ learning engagement in higher vocational colleges. These studies seem to have neglected the fact that SPTS may vary from different disciplines or age groups. Given aforementioned three considerations, EFL learners might require more, and possibly, different support from their teachers to overcome the challenges of learning a foreign language.

Therefore, this study adopted a mixed method approach to construct and validate an SPTS scale specifically for the context of EFL learning in China. To achieve this aim, the study will delve into the following two research questions:

RQ1. What are the dimensions of college SPTS within the context of EFL learning?

RQ2. How valid and reliable is the SPTS scale?

Methodology

The study consists of two stages where stage one is focused on producing items for the scale, and stage two comprises the scale validation. To produce an item pool, a semi-structured interview was deployed; to validate the initial SPTS scale, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were conducted.

Stage One: Scale Items Production

Constructing the Initial Items Pool

A review of the literature was first implemented to collect relevant scales, extract keywords and themes related to SPTS, and further clarify the connotation of SPTS. The outline of the semi-structured interview was drafted based on the key words and themes extracted from the literature (Chai & Gong, 2013; Chen, 2005; Chi, 2017; Ouyang, 2005; see Appendix A).

Semi-structured interviews were then carried out at a university (in central China). A total of 86 non-English majors were randomly selected to participate in the interview. There were 38 male interviewees, accounting for 44% and 48 female interviewees, accounting for 56%, with the average age being 19. Among all the interviewees, 19 students major in science (22%), 24 in technology (28%), 28 in engineering (33%) and 15 in humanities (17%).

Prior to the interviews, participants were briefed that the study aimed to explore their opinions and perspective about how they feel and think about language teacher support based on their college English learning experience. Interviewees were encouraged to elaborate on topics, experiences, and sentiments they deemed relevant. The duration of each interview ranged from 15 to 60 min. All interviews took place in the school canteen or cafe and began with open-ended questions, which were then gradually narrowed down to specific inquiries.

Qualitative analysis was utilized to scrutinize the data collected through semi-structured interviews. An inductive approach (Braun & Clarke, 2006) was adopted; that is, by reading the transcript iteratively, we identified the categories related to teachers’ support that emerged from the data. Then these categories (e.g., encouraging students; providing learning strategies; expanding the language and cultural knowledge) were formulated into statements, which became the initial item pool comprised of 40 items for the SPTS scale. To ensure the reliability of the qualitative data analysis, two researchers coded the data together and solved the divergence through discussion.

Developing and Refining the Initial Scale

To validate the initial items and ensure the content validity of the construct, we consulted six professors who hold doctoral degrees in applied linguistics and have extensive research experience in second language acquisition. Under their guidance, items that share similar meanings were deleted or rephrased. For instance, the items “The English teacher teaches us some learning strategies for the CET-4/6 exams” and “The English teacher popularizes knowledge related to English exams for studying abroad” were merged into “The English teacher provides some learning strategies for various English tests” as they were both related to English exam-taking. Besides, those items obscure in meaning were also rephrased. Finally, a total of 31 items were yielded. Each item was based on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree).

Stage Two: Scale Validation

In this phase, quantitative analysis was conducted by SPSS 23.0 and AMOS 26.0. Firstly, according to the initial scale we have obtained in stage one, we distributed the questionnaire to students. Secondly, to address the issue of what potential dimensions of SPTS are among non-English majors, SPSS 23.0 was applied to conduct EFA. Thirdly, to examine the internal consistency and construct validation of the self-designed scale, AMOS 26.0 was utilized for conducting CFA.

Conducting the Questionnaire Survey

Using a snowball sampling method, an online questionnaire was delivered via a professional online platform called Wenjuanxing (https://www.wjx.cn/). Calls for participation were sent through emails to English colleagues, inviting them to help by forwarding the link to their colleagues and students. The aim was to amplify the number of participants and encourage students’ involvement in completing the questionnaire. The questionnaire remained open online from October to December in 2022.

A total of 1,089 questionnaires were distributed. After careful examination, 327 invalid questionnaires were removed. This resulted in 762 valid questionnaires with a recovery rate of 70%. The respondents were mainly from the first and second year of non-English majors, for whom college English is a compulsory course. Detailed demographic information is shown in Table 1.

Table 1.

Participants’ Profile.

Demographic information (n = 762)	n	Percentage
Gender
Male	350	46
Female	412	54
Average age	19.4
Education background
Vocational college students (years 1 and 2)	54	7
Undergraduate students (years 1, 2, and 3)	617	81
Postgraduate students (year 1)	91	12
Universities
Subordinating to Ministry of Education	19	2.5
Subordinating to provincial government	452	59.3
Privately supported	146	19.2
Higher vocational colleges	23	3.0
Others	122	16
Disciplines
Science	134	18
Technology	177	23
Engineering	237	31
Humanities	214	28

Note. Others refer to students who didn’t disclose the type of university they are in.

Conducting EFA

To assess whether the 31 observed variables can effectively differentiate respondents, an item analysis was conducted at the outset. First, the total scores of the respondents on the questionnaire were ranked in high-to-low order. Roughly one quarter of the respondents with the highest total scores were designated as the high-scoring group, and the other one quarter of the subjects with the lowest total scores were designated as the low-scoring group. An independent samples t-test was conducted to determine the differences between the two groups on each item. The results showed that the 31 observed variables could effectively differentiate the two groups of students. These items in the questionnaire, therefore, demonstrated satisfactory discriminant validity.

To demonstrate convergent validity, we conducted an internal consistency reliability analysis. The Cronbach’s alpha value was found to be .974 (α > .70), indicating that the initial scale had good internal consistency and reliability (Kaplan & Saccuzzo, 2017).

Prior to conducting EFA, we performed a Kaiser-Meyer-Olkin (KMO) test and Bartlett’s test on the 31 observed variables. The results indicated that the KMO value was .974, which is acceptable for EFA. Additionally, Bartlett’s Test of Sphericity was significant (Sig. = .000; p < .05), indicating a strong correlation among the 31 variables. Therefore, the 31 observed variables were suitable for EFA.

Subsequently, principal component analysis was selected as the factor extraction method, while Varimax with Kaiser normalization was chosen as the rotation method, with absolute values below 0.5. We used the results of the Total Variance Explained (eigenvalue greater than 1) and Scree Plot to determine the number of factors and found that extracting three factors were most suitable (see Table 2 and Figure 1), which explained a total of 67.187% of the variance.

Table 2.

Total Variance Explained.

Component	Initial eigenvalues			Extraction sums of squared loadings			Rotation sums of squared loadings
Component	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %
1	17.763	57.300	57.300	17.763	57.300	57.300	7.877	25.409	25.409
2	1.892	6.102	63.402	1.892	6.102	63.402	7.665	24.726	50.134
3	1.173	3.785	67.187	1.173	3.785	67.187	5.286	17.053	67.187
4	0.938	3.026	70.213
5	0.801	2.583	72.796
6	0.730	2.354	75.150
7	0.619	1.998	77.149
8	0.565	1.824	78.973
9	0.507	1.635	80.608
10	0.447	1.443	82.051
11	0.412	1.328	83.380
12	0.398	1.284	84.663
13	0.373	1.203	85.866
14	0.367	1.182	87.048
15	0.357	1.151	88.199
16	0.347	1.120	89.319
17	0.317	1.024	90.343
18	0.289	0.931	91.275
19	0.277	0.895	92.169
20	0.269	0.867	93.036
21	0.258	0.834	93.869
22	0.248	0.801	94.671
23	0.227	0.732	95.403
24	0.222	0.716	96.119
25	0.215	0.695	96.814
26	0.203	0.656	97.470
27	0.182	0.586	98.055
28	0.164	0.529	98.585
29	0.161	0.519	99.104
30	0.146	0.472	99.576
31	0.131	0.424	100.000

Note. Extraction method: principal component analysis.

Figure 1.

Scree plot.

Table 3 presents the rotated component matrix, which indicates that item 8 did not load onto any factor, and was, therefore, removed from the component matrix. The loading values for the first factor (Q1–Q7) range from .510 to .796, the second factor (Q9–Q17) from .532 to .815, and the third factor (Q18–Q31) from .549 to .726.

Table 3.

Rotated Component Matrix.^a

	Component
	1	2	3
Q20	.796
Q19	.773
Q28	.762
Q23	.690
Q21	.684
Q27	.664
Q29	.655
Q24	.645
Q31	.638
Q30	.634
Q26	.617
Q18	.566
Q25	.531
Q22	.510
Q13		.815
Q15		.808
Q12		.803
Q14		.792
Q16		.769
Q10		.726
Q11		.639
Q17		.626
Q9		.532
Q3			.726
Q1			.722
Q4			.671
Q2			.650
Q6			.581
Q5			.553
Q7			.549
Q8

Note. Extraction method: principal component analysis; Rotation method: Varimax with Kaiser normalization.

Rotation converged in seven iterations.

Conducting CFA

To further examine the relationship between the latent variables of SPTS and its 30 observed variables, we conducted CFA. We randomly selected 50% of the samples using the select cases function in SPSS, which automatically formed a fitness test dataset with 379 samples. This followed the suggested criteria, that is, a ratio of 1:10 between the number of variables and participants (Nunnally, 1994).

To measure the fit structure of SPTS scale, we followed a three-step procedure proposed by Bagozzi and Yi (1988): (1) Preliminary Fit Criteria, (2) Overall Model Fit, and (3) Fit of Internal Structure of Model.

Regarding the preliminary fit criteria, we examined the factor loadings and correlation coefficients. The factor loadings ranged from .699 to .913, which met the recommended threshold of .50 <γ <.95 (Tseng et al., 2006). The correlation coefficients among the variables ranged from .335 to .845, all reaching a significant level (|r| < 1, p < .05; Bagozzi & Yi, 1988). These results indicate that the three subscales had good discrimination with each other and no empirical redundancy, and the appropriateness of the factor loadings portrays good acceptability of the construct validity of SPTS scale.

In terms of the Overall Model Fit, eight standard goodness-of-fit (GOF) indices, comprised of χ²/df, GFI, AGFI, SRMR, RMSEA, CFI, TLI, and NFI, were computed to evaluate how well the SPTS scale fits the sample data.

The χ²/df index is used to determine whether the hypothetical model should be accepted or rejected. Since our sample size was higher than 200, we considered an acceptable threshold for χ²/df to be lower than 5, based on the guidelines given by Han et al. (2022) and Li (2021). GFI and AGFI value equal to or greater than 0.80 is considered a good model fit (Doll et al., 1994; Gefen et al., 2000). SRMR coefficient below 0.07 (Hair et al., 2009) indicates a preferable fit, while RMSEA value equal to or lower than 0.08 suggests a good fit. CFI, TLI, and NFI coefficient higher than 0.90 (Hair et al., 2009; Hu & Bentler, 1999; Kline, 2023) is considered a general cut-off for good fit.

The initial results showed that some of the fitness indices did not meet the recommended guidelines. As a result, the model was modified based on covariances, with a modification index threshold of 10, and six observed variables (Q1, Q9, Q17, Q21, Q22, and Q28) were removed (see Table 4). It should be noted that during the model modification process, the MI values between multiple error terms which belong to the same level are relatively high. After considering their theoretical rationality, they were connected with double arrows to obtain better fit indices. In addition, the MI values corresponding to “F3” and “e11” is greater than 10. However, considering that the factor corresponding to “e11” and its observed variables includes the five core elements of EFL learning “listening, speaking, reading, writing, and translation,” and “e11” and its observed variables specifically represent “speaking ability”; thus, deleting “e11” and its observed variables would have a certain impact on the measurement validity of the theoretical model. Additionally, even if “e11” and its corresponding observed variables are deleted, the impact on the re-calculated fit indices is minimal. Therefore, it is considered appropriate to retain “e11” and its corresponding observed variables. The specific items that have been deleted are presented in Appendix B.

Table 4.

Factors and Corresponding Items.

Factors	Items
Factor 1	Q2, Q3, Q4, Q5, Q6, Q7
Factor 2	Q10, Q11, Q12, Q13, Q14, Q15, Q16
Factor 3	Q18, Q19, Q20, Q23, Q24, Q25, Q26, Q27, Q29, Q30, Q31

Note. Items Q1, Q9, Q17, Q21, Q22, and Q28 have been deleted.

Hair et al. (2009) recommended a minimum sample size of 150 for SEM analysis with no more than seven constructs. In this study, the sample size was 379 and three constructs were proposed, thus allowing for further testing of the measurement model using AMOS 26.0. A structural equation model (Figure 2) was developed based on the results of EFA, and GOF was assessed using eight indices, namely χ²/df, GFI, AGFI, SRMR, RMSEA, CFI, TLI, and NFI.

Figure 2.

Graphical representation of the three-factor model and factor loadings.

CFA results have shown that the construct of SPTS scale, which consists of 24 observed variables, is well-supported by the data collected. All indices, including χ²/df, GFI, AGFI, CFI, RMSR, RMSEA, IFI, TLI, and NFI, have met the recommended guidelines (see Table 5). The adjusted goodness of fit index (AGFI) value of 0.878 is slightly lower than the recommended 0.90 but still acceptable (Gefen et al., 2000). All standardized regression weights are above 0.50.

Table 5.

Overall Model Fit.

Fit indices	Value	Recommended guidelines	References
χ²/df	2.138	<5	Han et al. (2022) and Li (2021)
GFI	0.903	>0.80	Doll et al. (1994)
AGFI	0.878	>0.80	Doll et al. (1994), Gefen et al. (2000)
SRMR	0.033	≤0.08	Hair et al. (2009) and Hu and Bentler (1999)
RMSEA	0.055	<0.07	Hair et al. (2009)
CFI	0.968	≥0.90	Hair et al. (2009) and Hu and Bentler (1999)
TLI	0.963	≥0.90	Hu and Bentler (1999) and Kline (2023)
NFI	0.941	≥0.90	Hair et al. (2009)

Validity analysis was conducted on each construct, which yielded satisfactory results. The constructs were then evaluated for reliability in Table 6, which included Cronbach’s alpha, composite reliability (CR), and average variance extracted (AVE). Table 6 indicates that all factors had high reliability values (α > .7; Kaplan & Saccuzzo, 2017), and all CR values met the recommended threshold (CR > .7; McDonald, 1978).

Table 6.

Reliability of the Measurement Model.

Factor	No. of items	Cronbach’s alpha	Composite reliability	AVE
Factor 1	6	.880	.914	.435
Factor 2	7	.945	.910	.532
Factor 3	11	.951	.827	.409

All constructs showed good discriminant validity through AVE analysis (AVE > .4; Yang et al., 2022). In this study, the minimum Cronbach’s alpha, CR, and AVE values, as shown in Table 6, were .880, .827, and .409, respectively, all of which are above the recommended threshold, indicating that the constructs were reliable.

Based on the above indices, “Factor 1,”“Factor 2,” and “Factor 3” have a good correspondence with their respective observed variables, demonstrating that the measurement model is stable and reliable.

We tested the correlation between the three dimensions (see Table 7). The correlation coefficients for Factor 1, Factor 2, and Factor 3 ranged from 0.717 to 0.809 and all reached a significant level (|r| < 1, p < .05; Bagozzi & Yi, 1988), indicating both a degree of independence and a close relationship between the three dimensions.

Table 7.

Pearson Correlations of the Three Factors.

Factor	1	2	3
Factor 1	1
Factor 2	0.717**	1
Factor 3	0.809**	0.756**	1

Correlation is significant at the .01 level (two-tailed).

Forming the Final SPTS Scale

EFA and CFA resulted in a three-factor model that fits the data well. These three factors were named based on the measurement items included in each dimension, which are respectively emotional, intellectual and social support.

The first factor was named “emotional support,” as the items (e.g., English teacher cares about my study) incorporated in this dimension were all related to emotions. Emotional support in the present study involves providing positive attention, care, and encouragement to students in order to foster a supportive learning atmosphere and build emotional connections with them (Jiang et al., 2018; Ouyang, 2005; Rosiek, 2003). The items comprised in the second factor were directly related to English learning (e.g., English teacher will guide on listening strategies.). Drawing on the existing research results (Jiang et al., 2018; Ouyang, 2005), the second factor was named “intellectual support,” which is defined in the present study as the provision of services by teachers to enhance learners’ knowledge and skills by utilizing teachers’ own knowledge structure and teaching strategies (Jiang et al., 2018). The third factor contained items that emphasized interaction (e.g., In classes, English teacher often provides us with opportunities for teacher-student interaction), which was named “social support.” In the present study, social support refers to the help and assistance that teachers provide to learners through various types of interaction, including teacher-student interaction and the promotion of student-student interaction (Jiang et al., 2018).

Emotional support consists of six items; intellectual support comprises seven items; social support encompasses 11 items. Eventually, the final SPTS scale, including a total of 24 items, was yielded. Each item was based on a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree).

Discussion

Gaining an understanding of students’ perceptual information is crucial for fully comprehending their cognitive processes (McArthur & Baron, 1983). To gain insights into the psychological changes and cognitive processes of learners during the English learning process, this study conducted an in-depth exploration of the underlying dimensions of SPTS.

We constructed and validated a new SPTS scale specifically designed for EFL learning. Research results revealed that there were three dimensions included in SPTS: emotional, intellectual, and social support. The results of this study are supported by SDT, which posits that if the social environment supports the satisfaction of individuals’ basic psychological needs, namely competence and relatedness, it can positively influence their behavioral motivation (Niemiec & Ryan, 2009). In this research, we identified that SPTS is a three-dimensional construct, consisting of students’ perceived teacher emotional, intellectual, and social support, which align with the basic psychological needs of competence and relatedness. Specifically, emotional support corresponds to the need for relatedness, emphasizing positive attention, care, encouragement, and a sense of belonging while intellectual and social support echo the need for competence. Since students’ competence can be supported by educators through various activities, appropriate tools, feedback, and learning strategies (Niemiec & Ryan, 2009), thus the need for competence can be seen as encompassing both intellectual and social support. These supports can be fulfilled respectively through teachers’ provision of services that enhance learners’ knowledge and skills, as well as their assistance in promoting various forms of interaction. According to SDT, with teachers’ emotional, intellectual, and social support (SPTS), learners who lack intrinsic motivation can be re-motivated and re-engaged in their learning process. This support increases the likelihood of achieving beneficial effects on their learning effectiveness.

The importance of SPTS in college EFL learning is multifaceted, significantly impacting students’ psychological well-being (Gao et al., 2023), academic achievement (Bai et al., 2019), willingness to communicate (Hejazi et al., 2023), and foreign language anxiety (Liu et al., 2023). Unlike in general education, where teacher support is broadly beneficial, SPTS in college EFL learning exhibits distinct characteristics due to the unique challenges of acquiring a second language. There is a body of research on SPTS, among which researchers have elucidated the importance of emotional support (Derakhshan et al., 2023; Gao et al., 2023; Hejazi et al., 2023; Liu et al., 2023; Wu et al., 2023; Zhou et al., 2023). More importantly, an innovative finding in this study is that two new factors have emerged—intellectual support and social support.

In the existing literature, emotional support is a commonly acknowledged factor in SPTS (Chai & Gong, 2013; Chen, 2005; Jiang et al., 2018; Liu & Li, 2023; Ouyang, 2005; Shelton, 2003). The present research results lend further support to these research findings. This revealed that regardless of the discipline, students’ perceived degree of teachers’ emotional support exerts great importance on their school learning. Moreover, emotional support from teachers is crucial in the field of EFL context, as it positively influences both psychological and behavioral dimensions of EFL learners. For instance, studies have shown that teacher emotional support significantly predicts L2 burnout and L2 grit (Derakhshan et al., 2023; Wu et al., 2023), enhances psychological well-being (Gao et al., 2023), and promotes learning engagement (Yang & Du, 2023; Zhou et al., 2023). Additionally, this type of support can alleviate foreign language anxiety, which is a common barrier to language acquisition (Hejazi et al., 2023; Liu et al., 2023). By reducing anxiety, students are more likely to engage actively and confidently in language learning activities. The interview results also conclusively revealed that among the 86 participants, a significant majority of 90% expressed that teachers’ emotional support positively and profoundly impacted their motivation to learn.

Additionally, intellectual support from teachers also plays a vital role in EFL learning. Bai et al. (2019) confirmed that students’ perception of intellectual support from teachers is positively linked to their English learning performance. Unlike in traditional teaching and learning, students in the study were aware of their weaknesses in listening, speaking, reading, writing, and translation, but lacked effective learning methods to tackle their problems. The reason for the differences may lie in the fact that, unlike in other disciplines, the scarcity of opportunities for EFL students to use English highlights the importance of teachers creating an immersive English-speaking environment, which is key to fostering students’ success in learning the language (Wu & Wu, 2008); thus, EFL learning further requires intellectual guidance from teachers to help students master the knowledge they have acquired (Brown, 2014). As one interviewee put it, “English teachers never taught us anything about test-taking, such as how to use a smartphone to practice English listening or which listening skills can help us score higher marks on exams. They only focus on textbook content and limit their teaching to the course itself without discussing learning methods.” It is evident that students place great importance on the learning methods and approaches offered by their English teachers.

On top of that, social support serves as a prerequisite for the frequent use of the target language, playing an irreplaceable role in students’ language learning. Recent literature has frequently highlighted the reticence and passivity observed among Asian learners, particularly those from East Asian backgrounds (Cheng, 2000; Sang & Hiver, 2021). In the context of EFL learning, it is crucial for teachers to recognize the fundamental essence of language itself—a tool for communication. English, as both a learning tool and a learning goal for L2 learners, implies that the appropriate way of learning English is using it. During the process of using English, teachers’ social support plays a vital role in creating an effective EFL learning environment. Hejazi et al. (2023) confirmed that students who perceived stronger teacher support were more willing to communicate in their classes. Besides, Rui and Liu (2023) noted that students who feel supported by their teachers are more confident in their English listening and speaking abilities. Based on this, English teachers should focus on providing opportunities for students to engage in English-speaking activities. They can incorporate audio or video materials to enhance these opportunities aiming to establish a supportive and comfortable atmosphere. Such an environment enables students to immerse themselves more fully in the language and fosters the development of confidence to express themselves in English.

However, there is a discrepancy between the present research and the study by Liu and Li (2023). While our findings on emotional support and intellectual support are consistent with theirs, the third factor, social support identified in our study, differs from the instrumental support identified by Liu and Li (2023). This disparity can possibly be attributed to the varying learning stages of the participants. Different stages of learning often necessitate distinct syllabi and characteristic pedagogical approaches. High school EFL learning in China is primarily exam-oriented, whereas college EFL education emphasizes its practical social function (Long & Xu, 2023). Considering that our research focused on college EFL learners, while Liu and Li’s study centered on secondary school students, it is plausible that student perceptions of teacher support may vary between these educational contexts.

This comprehensive support fosters a conducive learning environment where students feel encouraged to practice and refine their language skills regularly. Teachers’ emotional support can provide psychological incentives for students’ learning, enhancing their motivation and self-discipline (Roorda et al., 2011; Wang & Eccles, 2012). Intellectual support, provided by teachers through the provision of learning strategies, has a positive correlation with the performance of EFL learners in multiple areas such as listening, oral speaking, etc. (Ngo, 2019; Yang, 1999). Social support from teachers could lead to increased participation in learning, including heightened attentiveness, active participation in both in-class and out-of-class tasks and activities, and a positive classroom demeanor (Wang & Eccles, 2012). By understanding and leveraging these distinct characteristics of SPTS, educators can better support EFL learners in overcoming the challenges of language acquisition and achieving their learning goals.

Conclusion and Implications

This study has investigated the dimensions of college SPTS under the context of EFL learning. We have verified that SPTS is a tripartite construct entailing emotional, intellectual, and social support. Among these three dimensions, emotional support from teachers involves providing positive attention, care, and encouragement to students in order to foster a supportive learning atmosphere and build emotional connections with them, intellectual support refers to the provision of services by teachers to enhance learners’ knowledge and skills by utilizing teachers’ own knowledge structure and teaching strategies, and social support denotes the help and assistance that teachers provide to learners through various types of interaction, including teacher-student interaction and the promotion of student-student interaction. SPTS scale can serve as a reference to college EFL teachers as it provides theoretical guidance to develop targeted teaching interventions, which is of great practical significance for improving the quality of foreign language teaching.

The findings of this study have several implications for future investigations of college EFL SPTS. First of all, the interviews conducted in the present study were restricted to the participants from the same university, which might bring the possibility of potential bias or influence on the interview outcomes. Secondly, the study focused on the current perception of teacher support among L2 learners in a cross-sectional manner, lacking longitudinal exploration into the dynamic changes of SPTS over time. Additionally, future studies could examine the relationship between SPTS and other closely related variables, such as self-efficacy, learning performance, engagement, burnout, well-being, etc.

Footnotes

We appreciate Ms. Ming Lv,Mr. Gang Yao,Ms. Xi Lin,Mr. Jun Zhang,Ms. Xiaoxiao Liu,and all those who kind-heartedly helped us to distribute the questionnaires and collect data for this study.

Data Availability Statement included at the end of the article

Author Contributions

JL: Methodology;In-depth data analysis,Original draft preparation,Revising and editing;ZL: Conceptualization,Data collection,Methodology,Primary data analysis,Supervision,Revising and editing.

Declaration of Conflicting Interests

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

Funding

The author(s) disclosed receipt of the following financial support for the research,authorship,and/or publication of this article: This work was supported by (1) the Philosophical and Social Science Research Project of the Ministry of Education of the People’s Republic of China [22YJC740053];(2) the Philosophical and Social Science Research Project of the Department of Education of Hubei Province [23Q030];(3) the PRC’s Ministry of Education’s Industry-Academia Collaborative Education Project [230806121030540];and (4) China’s Higher Education Foreign Language Teaching Research Project [2022HB0015].

Ethical Approval

Ethical approval was not necessary and required for this study because it involves no sensitive personal and confidential information,clinical intervention,and human experiments. Besides,informed consent was obtained from all the participants before the survey and the interview. All the data were kept anonymous.

ORCID iD

Zaibo Long

Data Availability Statement

The datasets of the study are not currently available,as this research is only one part of a larger in-progress research project. The data will still be used and analyzed in other studies/articles.

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Construction and Validation of a Scale to Measure College EFL Students’ Perceived Teacher Support

Abstract

Plain Language Summary

Keywords

Introduction

Literature Review

Methodology

Stage One: Scale Items Production

Constructing the Initial Items Pool

Developing and Refining the Initial Scale

Stage Two: Scale Validation

Conducting the Questionnaire Survey

Conducting EFA

Conducting CFA

Forming the Final SPTS Scale

Discussion

Conclusion and Implications

Footnotes

Author Contributions

Declaration of Conflicting Interests

Funding

Ethical Approval

ORCID iD

Data Availability Statement

References