System 122 (2024) 103253
0346-251X/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Profiling L2 students ’ writing self-efficacy and its relationship with their writing strategies for self-regulated learning
Jianhua Zhang
a,b, Lawrence Jun Zhang
b,*aSchool of Foreign Languages, Sichuan University of Arts and Science, China
bFaculty of Education and Social Work, University of Auckland, New Zealand
A R T I C L E I N F O Keywords:
Profiles of writing self-efficacy SRL writing strategies L2 students Latent profile analysis
A B S T R A C T
The current study aimed to examine the relationship among writing self-efficacy, writing stra- tegies for self-regulated learning (SRL), and writing achievement in L2 students by adopting latent profile analysis and path analysis. A sample of 391 L2 students from two universities in Western China was recruited to participate in the current study. They were required to respond to the Genre-Based L2 Writing Self-Efficacy Scale and the Writing Strategies for Self-Regulated Learning Questionnaire and also write a given-prompt argumentative essay. Three profiles of writing self- efficacy were identified through latent profile analyses: “Low on All Self-efficacy”, “Average on All Self-efficacy”, and “High on All Self-efficacy”. Moreover, ANOVA and Welch’s Tests revealed that those identified profiles were significantly distinct in writing self-efficacy, SRL writing strategies, and writing achievement. Path analyses also demonstrated the profile differences in the predictive effects of writing self-efficacy on SRL writing strategies and the predictive effects of writing self-efficacy and SRL writing strategies on writing achievements. Methodological and pedagogical implications were discussed.
1. Introduction
The importance of self-efficacy for acquiring writing competence and skills has been well-established (e.g., McCarthy et al., 1985;
Pajares et al., 1999; Pajares & Johnson, 1994; Pajares & Valiante, 1997, 1999). Writing self-efficacy concerns students’ self-confidence in their capabilities to complete writing tasks. Given the differences between first language (L1) writing and second or foreign language (hereafter referred to as L2) writing, Teng et al. (2018) put forward a model of writing self-efficacy covering three dimensions: lin- guistic, self-regulatory, and performance.
Accumulated studies in the field of L2 writing have examined the predictive effect of writing self-efficacy on writing achievement (e.g., Sun & Wang, 2020; Teng et al., 2018; Woodrow, 2011; Zabihi, 2018). However, it might be difficult to draw a definite conclusion mainly because of some issues in measuring writing self-efficacy. Therefore, the conception of writing self-efficacy was elaborated to address the task-specificity issue (Zhang et al., 2023).
Writing self-efficacy has been conceived to be closely related to writing self-regulation (e.g., Zimmerman, 2000; Zimmerman &
Risemberg, 1997). Students employ various techniques or strategies to manage their environmental, behavioural, and personal pro- cesses to facilitate their self-regulation of writing (Glaser & Brunstein, 2007; Graham & Harris, 2000). SRL writing strategies attracted scholars to concentrate their attention on factors influencing L2 students’ use of such strategies (e.g., Bai, 2015; Bai et al., 2014, 2020;
* Corresponding author. Faculty of Education and Social Work, University of Auckland, 10 Symonds Street, Auckland 1010, New Zealand.
E-mail addresses: [email protected], [email protected] (J. Zhang), [email protected] (L.J. Zhang).
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https://doi.org/10.1016/j.system.2024.103253
Received 13 August 2023; Received in revised form 4 February 2024; Accepted 6 February 2024
Teng & Zhang, 2018). The facilitative effect of SRL writing strategies on L2 students’ writing self-efficacy has been well examined (e.g., Bai & Guo, 2018; Guo & Bai, 2019). To our limited knowledge, only one study explicitly investigated the predictive effect of L2 students’ writing self-efficacy on SRL writing strategies (i.e., Teng, 2022). Therefore, it is imperative to conduct more studies to confirm this predictive effect.
Studies have also demonstrated that L2 students’ writing self-efficacy and utilization of SRL writing strategies exerted significant predictive impacts on their writing achievement or proficiencies (e.g., Sun & Wang, 2020; Teng & Huang, 2019; Teng & Zhang, 2016;
Zhang et al., 2023). To our limited knowledge, few scholars have explored the possible heterogeneity of the predictive effect of writing self-efficacy and SRL writing strategies on L2 writing achievements by adopting the variable-centered approach, which often focuses on the relationships between individual variables isolatedly. Compared with the variable-based approach, the person-centered approach allows a researcher to investigate multiple variables simultaneously and identify subgroups or profiles of individuals characterized by similar response patterns across these variables (Howard & Hoffman, 2018). Scholars adopting the person-centered approach utilized different statistical methods, such as cluster analysis and latent profile analysis (LPA). By employing LPA, Kim et al.
(2015) detected three profiles of self-efficacy for learning English among L2 students: high, medium, and low self-efficacy profiles, thus offering us insights into the group-level differences in self-efficacy. Therefore, it would be significant to examine whether the same profiles may be shared by L2 students’ writing self-efficacy in that such profiling might enable us to understand the above-mentioned potential heterogeneity, thus presenting a more detailed picture of the effects of writing self-efficacy and SRL writing strategies on L2 achievement.
To fill the research lacuna in the literature as the main objective of this study, we aimed to identify the various profiles of writing self-efficacy as well as the heterogeneity in the relationship among writing self-efficacy, SRL writing strategies, and writing achievement in the population of L2 students. Specifically, we intended to detect the latent profiles or subgroups of writing self- efficacy, the subgroup differences in the predictive influence of writing self-efficacy on SRL writing strategies, and the combined effects of writing self-efficacy and SRL writing strategies on L2 writing achievements.
2. Literature review 2.1. Self-efficacy in L2 writing
Self-efficacy is conceived as a crucial learning incentive that might exert effective predictive impacts on academic achievement, as evidenced in educational psychology (e.g., Zimmerman, 2000). Specifically, it might determine what learners could plan to achieve, how much effort and time they could invest and devote, how they might evaluate their goals, effort, and persistence, and how they might adjust their emotions (e.g., enjoyment, anxiety, and boredom) (Pajares, 2003).
Scholars commonly acknowledged that self-efficacy plays a crucial role in developing writing competence and skills (e.g., McCarthy et al., 1985; Pajares et al., 1999; Pajares & Johnson, 1994; Pajares & Valiante, 1997, 1999). Writing self-efficacy relates to how student writer might perceive their abilities to finish writing assignments (e.g., letters, essays, proposals, thesis, and dissertations) (Pajares, 2003). Based on a synthetic review of previous studies, Bruning et al. (2013) reconceptualized writing self-efficacy by attaching particular importance to linguistic and psychological factors involved in the writing process, thus entailing three focal di- mensions: ideation, writing conventions, and self-regulation. Ideation self-efficacy was concerned with students’ self-confidence in their abilities to come up with new ideas, which might impact other writing processes due to their cyclic nature. Self-efficacy in writing conventions was concerned with students’ confidence in translating ideas within the constraints of generally accepted rules in a given language. Self-efficacy for self-regulation was concerned with how confident students might be in successfully utilizing self-regulatory strategies to respond to factors involved in the writing processes. Considering the differences between L1 and L2 writing, Teng et al.
(2018) reconceptualized writing self-efficacy from the cognitive-social perspective, entailing linguistic self-efficacy, self-regulatory self-efficacy, and performance self-efficacy. Based on this conceptualization, they developed the Second Language Writer Self-Efficacy Scale to gauge L2 students’ writing self-efficacy, which has been criticized for not tackling the task-specificity issue that was inherent with other scales for writing self-efficacy in L1 and L2 fields (Sun & Wang, 2020). To solve the issue, Zhang et al. (2023) developed the Genre-based Second Language Writing Self-Efficacy Scale by incorporating genre features of writing tasks, which examined four di- mensions of writing self-efficacy: linguistic, classroom performance, genre-based performance, and self-regulatory self-efficacy. They validated this scale against L2 students, demonstrating higher reliability and validity (i.e., construct validity, discriminative validity, and predictive validity).
Scholars were interested in writing self-efficacy and devoted their efforts to examining its relationship with writing achievements in the field of L2 writing. Woodrow (2011) initially investigated the relationship between writing self-efficacy and writing performance in tertiary Chinese-speaking EFL learners. Structural equation modelling showed that writing self-efficacy as a whole exerted a sig- nificant predictive effect on writing performance. Unfortunately, the drawback of this study might mainly lie in the design of the measurement of writing self-efficacy, specifically for including items concerning translation and writing tests without any claimed support, theoretical or other.
Later, Teng et al. (2018) also explored the relationship between writing self-efficacy and writing performance in tertiary Chinese-speaking EFL learners. Correlational analyses showed that (1) linguistic self-efficacy correlated moderately with L2 writing performance; (2) self-regulatory self-efficacy correlated weakly but positively with L2 writing performance; and (3) the correlation strength of performance self-efficacy with L2 writing performance lay in between the former two self-efficacies. In the meantime, Zabihi (2018) elaborated on the relationship between writing self-efficacy and L2 writing performance among Persian-speaking EFL undergraduate students by focusing on complexity, accuracy and fluency. They reported that writing self-efficacy had moderate to
strong correlations with three dimensions of L2 writing performance. Path analysis showed that (1) writing self-efficacy significantly predicted complexity, accuracy, and fluency of L2 writing performance, and (2) compared with working memory capacity, writing self-efficacy demonstrated different effects on dimensions of L2 writing performance. However, these findings might be mainly undermined by adopting the Self-Efficacy for Writing Scale in the L2 writing context without any modification because the scale was originally designed to assess L1 writers’ writing self-efficacy.
Recently, Sun and Wang (2020) probed the relationship between writing self-efficacy and L2 writing performance in Chinese-speaking EFL learners. The regression analysis revealed that writing self-efficacy could significantly predict L2 writing per- formance after controlling for socioeconomic status. However, the finding might be jeopardized by the measurement of L2 writing performance because the composite scores of both writing and translation tasks in the College English Test (CET), a national-wide English test in China, could be considered inappropriate for evaluating writing performance.
Therefore, based on these accumulated studies reviewed above, we can see that scholars might not draw an irrefutable conclusion regarding the predictive effects of writing self-efficacy on writing achievement among L2 students. Thus, it is imperative to conduct more studies to delineate the predictive effects. Moreover, scholars in previous studies mainly adopted the variable-based approach.
Although the variable-based approach could help us understand the relationship between writing self-efficacy and writing achieve- ment, the person-based approach might offer us a more nuanced grasp of that relationship by utilizing cluster analysis or LPA. Kim et al. (2015) initially examined the patterns of self-efficacy for learning English among Korean-speaking EFL learners by adopting LPA.
Through LPAs, they identified three groups of learners with high, medium, and low self-efficacy profiles. Later, Chen et al. (2022) examined profiles of L2 student writers based on their SRL writing strategies and writing self-efficacy using LPA and found three profiles: Efficacious self-regulators, moderate strategists, and unmotivated learners. However, their use of the z-standardized mean of the subscales for gauging SRL writing strategies and writing self-efficacy might obscure essential nuances and compromise the results concerning the profiles of L2 student writers. Thus, it is significant to investigate whether the same pattern of self-efficacy found by Kim et al. (2015) could exist among L2 student writers and, furthermore, whether L2 students in different profiles of writing self-efficacy might share the same predictive effect of writing self-efficacy on writing achievement.
2.2. SRL writing strategies in L2 writing
SRL is essential to learners’ perception of the orchestration of the cognitive, motivational, and emotional aspects of learning. It is
“an active, constructive process whereby learners set goals for their learning and then attempt to monitor, regulate, and control their cognition, motivation, and behaviour, guided and constrained by their goals and the contextual features in the environment” (Pintrich, 2000, p. 453). Effective SRL processes require learners’ active deployment of a range of strategies to help them intentionally activate, sustain, and adjust cognition, affect, and behaviour to achieve their learning goals (Zimmerman & Schunk, 2011).
Self-regulation could enable writers to manipulate the syntactic and semantic dimensions of writing (Bruning et al., 2013; Zim- merman & Bandura, 1994; Zimmerman & Kitsantas, 1999, 2002) and to improve their utilization of writing strategies (Brunstein &
Glaser, 2011; Graham & Harris, 2000). Scholars identified a great number of SRL strategies that writers utilized to manipulate environmental, behavioural, and personal processes identified in the self-regulation of writing (Glaser & Brunstein, 2007; Graham &
Harris, 2000; Zimmerman & Risemberg, 1997).
Research on the role of SRL writing strategies in the context of L2 writing mainly focused on factors influencing L2 students’ utilization of SRL writing strategies, for instance, proficiency (Bai et al., 2014), instruction (Bai, 2015), motivational regulation strategies (Teng & Zhang, 2018), gender and grade level (Bai et al., 2020), and writing self-efficacy, growth mindset, and task value (Bai et al., 2021). For instance, Bai et al. (2014) explored the relationship between SRL writing strategies and English proficiency in Singapore primary pupils. Analysis of variance (ANOVA) results revealed that students with different proficiency levels showed systematic differences in their use of SRL strategies. It was found that such strategies as planning, text-generating, monitoring and evaluating, revising, and resourcing contributed to participants’ English proficiency and writing development. Later, Bai (2015) revealed that strategy-based writing instruction could enhance pupils’ writing competence and SRL strategy use.
Moreover, Teng and Zhang (2018) further explored the relationship between SRL writing strategies and English writing proficiency by delineating the role of motivation regulation strategies among English majors in China. They asked the participants to write a given-topic argumentative essay, which was evaluated using the ESL Composition Profile. Mediation models revealed (1) motivation regulation strategies had a weak direct effect on writing performance; (2) motivation regulation strategies had an indirect effect on writing performance through cognitive and metacognitive strategies; and (3) motivation regulation strategies had significant, direct effects on cognitive, metacognitive, and social behavioural strategies. These findings implied that motivation regulation acted as a precursor and mediator of SRL writing strategies and had close interactions with cognitive, behavioural, and contextual factors involved in the SRL process. Recently, Bai et al. (2020) investigated the effect of gender, writing proficiency, and grade level on SRL writing strategies in upper graders in Hong Kong primary schools. The results of a three-way multivariate analysis of variance revealed that gender, writing proficiency, and grade level had impacts on students’ use of SRL writing strategies. Meanwhile, Bai et al. (2021) examined the relationships between motivational variables, SRL writing strategies, and writing competence in Hong Kong’s primary pupils. Structural equation modelling revealed that writing self-efficacy and growth mindset had a modest predictive effect on stu- dents’ use of SRL writing strategies, while task values showed complex predictive effects.
Only a limited number of studies focused on the predictive effect of SRL writing strategies on writing performance among L2 students. Teng and Zhang (2016) investigated the relationship between SRL writing strategies and writing proficiency in tertiary Chinese EFL learners. Multiple regression analyses revealed that text processing, idea planning, goal-oriented monitoring and eval- uating, feedback handling, motivational self-talk and emotional control were significant predictors of EFL learners’ English writing
proficiency. Recently, Teng and Huang (2019) investigated the predictive effect of SRL writing strategies on English writing profi- ciency in secondary EFL learners. Results of multiple regression analyses revealed that (1) nine SRL writing strategies as a whole acted as a strong predictor of secondary EFL learners’ writing proficiency, and (2) goal-oriented monitoring had the most significant pre- dictive effect on writing proficiency, which was followed by motivational self-talk, text processing, idea planning, interest enhance- ment, and emotional control in descending order. Meanwhile, Sun and Wang (2020) also investigated the predictive effect of the SRL writing strategy on writing proficiency in tertiary Chinese EFL learners. Hierarchical linear regression analysis revealed that SRL writing strategies had a predictive effect on EFL learners’ writing proficiency. However, to our limited knowledge, few scholars have devoted their attention to the possible group-level heterogeneity of the predictive effect of SRL writing strategies on L2 writing achievements.
2.3. The relationships between writing self-efficacy and SRL writing strategies
Zimmerman (2000) argued that motivation might play a crucial role in determining how students engage in self-regulated learning.
As mentioned in the above section, writing self-efficacy could act as an essential motivational factor. Therefore, writing self-regulation was postulated to be closely linked with writing self-efficacy (Zimmerman & Risemberg, 1997).
Researchers have concentrated on how SRL writing techniques helped L2 students develop their writing self-efficacy (e.g., Bai &
Guo, 2018; Guo & Bai, 2019). Bai and Guo (2018) investigated the effect of SRL writing strategies on writing self-efficacy in EFL learners in Hong Kong primary schools. Multiple ANOVAs revealed significant differences in writing self-efficacy among students with high, medium, and low use of SRL writing strategies. Structural equation modelling demonstrated that SRL writing strategies such as planning and self-monitoring exerted a more significant impact on EFL learners’ writing self-efficacy than other strategies. Further- more, Guo and Bai (2019) extended the effect of SRL writing strategies on writing self-efficacy in EFL learners in Hong Kong primary schools by adding another variable of intrinsic motivation. T-tests showed that high achievers reported more frequent use of SRL writing strategies than low achievers. In contrast, zero-order correlations indicated that SRL writing strategies positively correlated with intrinsic motivation and writing self-efficacy in both cohorts. Structural equation modelling revealed that SRL writing strategy use had different effects on high and low achievers’ writing self-efficacy and intrinsic motivation. For high achievers, the use of planning strategy had an impact on writing self-efficacy and intrinsic motivation, while the use of self-monitoring strategies exerted an impact only on intrinsic motivation. In contrast, for lower achievers, the use of self-motivating strategies and revising strategies impacted writing self-efficacy and intrinsic motivation. The finding suggested students with different writing achievements adopted different SRL writing strategies to enhance their motivation in writing.
However, the predictive effect of writing self-efficacy on the employment of SRL strategies among L2 students has seldom been explored. To our limited knowledge, only two studies explicitly examined such a predictive effect (e.g., Bai et al., 2022; Teng, 2022).
Bai et al. (2022) found a positive correlation between self-efficacy and SRL strategy use among EFL students from Hong Kong primary schools. By employing multiple regression analyses, Teng (2022) revealed that self-efficacy showed significant predictive effects on all kinds of self-regulated learning strategies (i.e., cognition, metacognition, and motivational regulation) except for strategies for social behaviour in the population of tertiary L2 student writers. Therefore, the result concerning the predictive impacts of writing self-efficacy on SRL writing strategies might be tentative, and therefore, it is necessary to conduct more studies to verify these effects.
Several issues in the literature concerning the relationship between writing self-efficacy, SRL writing strategy, and writing achievement have not been addressed appropriately. Firstly, the variable-centered approach adopted in the available studies assumes homogeneity across the entire sample; in other words, the participants were characterized by a single normal population distribution (Howard & Hoffman, 2018). This assumption might cover up the fact that the participants come from a population with different distributions. Secondly, the variable-centered approach focuses on the linear relationship between the above-mentioned variables across the entire sample and may overlook subgroup differences, which would not be valid or applicable in real situations, as demonstrated by Guo and Bai (2019). Thirdly, the results concerning the profiles of L2 student writers found in Chen et al. (2022) might be tentative and a little obscure, offering limited information about subgroups of L2 learners in terms of writing self-efficacy.
Therefore, to address the aforementioned issues, the current study was designed to examine the relationship among writing self- efficacy, SRL writing strategies, and writing achievement in L2 students by employing LPA. Different from the variable-centered approach, LPA recognizes and accommodates heterogeneity by identifying latent subgroups within the population, allows for the simultaneous examination of multiple variables, and identifies subgroups of individuals characterized by similar response patterns across these variables (i.e., Berlin et al., 2014; Peugh & Fan, 2013; Spurk et al., 2020). Accordingly, LPA acknowledges that different subgroups may exist with distinct response patterns and may facilitate us to uncover subgroup-specific relationships, capturing the complexity of relationships among variables and providing a more nuanced understanding of these relationships. As a result, LPA could facilitate us to examine the latent subgroups of L2 students in writing self-efficacy and then explore the nuanced subgroup differences in the predictive effects of writing self-efficacy on SRL writing strategies and the predictive effects of writing self-efficacy and SRL writing strategies on writing achievement, thus offering an elaborated understanding of these relationships and shedding insights on SRL-based instructional practices. Specifically, the current study intended to answer the following research questions:
RQ1 What are the profiles of L2 students’ writing self-efficacy?
RQ2 Are there differences in SRL writing strategies and L2 writing achievement across those profiles?
RQ3 Are there differences in the predictive effects of writing self-efficacy on the use of SRL writing strategies across those profiles?
RQ4 Are there differences in the predictive effects of writing self-efficacy and SRL writing strategies on L2 writing achievement across those profiles?
3. Methods 3.1. Participants
After agreeing to participate and signing formal consent, a total of 396 EFL students from two medium-ranking universities in Western China were voluntarily participated in the current study. They had been studying English as a foreign language for at least six years while also becoming fluent in Mandarin Chinese. They enrolled in a two-year integrated English course during their college, which concentrated on teaching five language skills (i.e., listening, speaking, reading, writing, and translating) in an integrated manner. They did not attend any courses specialized in English. Among the participants, 250 were females, accounting for 63.13 per cent, and 146 were males, accounting for 36.87 per cent. Their average age was 20.4, and they varied from 18 to 22. They were involved in different grades, of which 47.25 per cent (n =206) were freshmen, 25.46 per cent (n =111) were sophomores, and 27.29 per cent (n =119) were juniors. Their English proficiency ranged from pre-intermediate to intermediate.
3.2. Instruments
3.2.1. The genre-based L2 Writing Self-Efficacy Scale
Zhang et al.’s (2023) Genre-Based L2 Writing Self-Efficacy Scale was directly adopted to measure the participants’ writing self-efficacy. It was specifically designed to measure L2 learners’ self-perceived confidence for producing written essays where the participants could score their confidence on a 5-point Likert scale ranging from 1 (does not describe me) to 5 (describes me perfectly). As reported in Zhang et al. (2023), this scale demonstrated sound psychometric characteristics when used with Chinese-speaking EFL tertiary students.
Four facets of writing self-efficacy were examined in the current study: linguistic self-efficacy, self-regulatory self-efficacy, class- room performance self-efficacy, and genre-based performance self-efficacy. When the scale items were presented to the participants, they were all translated into Chinese, ensuring that they could comprehend each item entirely and preventing any potential mis- understandings. Similarly, translation and back translation were used to confirm and support the accuracy and equivalence of the translation. The Cronbach’s alpha coefficient, which indicated the scale’s strong internal consistency with a value of 0.922, was also used to assess the reliability of this instrument. Confirmatory factor analysis demonstrated the scale fitted well with the participants involved in this study (x2 =240.913; df =96; p <.001; x2/df =2.51; CFI =0.941; TLI =0.926; RMSEA =0.062 [0.052, 0.072]; SRMR
=0.056).
3.2.2. The Writing Strategies for Self-Regulated Learning Questionnaire
The Writing Strategies for Self-Regulated Learning Questionnaire designed by Teng and Zhang (2016) was utilized to gauge the participants’ employment of SRL writing strategies, including text revising, peer learning, feedback handling, interest enhancement, and motivational self-talk. We modified the original questionnaire by deleting the items with factor loadings lower than 0.5, with 23 items (items 1, 7, 8, 9, 10, 12, 11, 13, 15,16, 17, 18, 19, 23, 26, 30, 31, 32, 33, 38, 39, 40 on the initial list) being removed. The modified questionnaire focused on five specific SRL writing strategies: Text revising, peer learning, feedback handling, interest enhancement, and motivational self-talk. With the help of the revised questionnaire, participants could report their use of SRL writing strategies on a 7-point Likert scale ranging from 1 (not at all true of me) to 7 (very much true of me). The reliability of the revised questionnaire was also assessed by Cronbach’s alpha coefficient, whose value was 0.895, suggesting the sound internal consistency of this scale. The result of confirmatory factor analysis showed that the revised questionnaire fitted well with the participants involved in this study (x2 =248.297; df =109; p <.001; x2/df =2.278; CFI =0.928; TLI =0.910; RMSEA =0.057 [0.048, 0.067]; SRMR =0.053).
3.2.3. English writing test
Participants’ writing achievements were evaluated by a writing test. The participants were required to complete a given-prompt argumentative writing task with at least 150 words. They were also asked to complete the writing task online within 40 min due to the constraints of the COVID-19 pandemic. The writing task was selected partially because the participants were more familiar with argumentative writing than other writing tasks and partially because argumentative writing was frequently tested in CET and thus was sufficiently practised among the participants. Therefore, one topic regarding the use of the Internet in education from the item pool of CET 4 was selected (see Appendix A for details).
Jacobs et al.’s (1981) ESL Composition Profile, one of the extensively used analytical scoring rubrics in the field of L2 writing, was employed to gauge participants’ writing achievement (e.g., Chen & Zhang, 2019; Huang & Zhang, 2020; Teng, 2022; Xu et al., 2022, 2023a, 2023b). This scoring rubric covers five dimensions of writing performance: content, organization, language, vocabulary, and mechanics. Compared with the 0–15 holistic scoring scale employed for the CET writing, this 0-100 rubric assigns different weights to the aforementioned dimensions: 0.3 for content, 0.2 for organization, 0.25 for language, 0.2 for vocabulary, and 0.05 for mechanics and would provide more details and distinctions about the participants’ writing achievement.
3.3. Procedure
The current study incorporated two sessions, which were implemented in a day. In session one, the Genre-Based L2 Writing Self- Efficacy Scale and the Writing Strategies for Self-Regulated Learning Questionnaire were given to the participants to elicit their self- perception of writing self-efficacy and the use of SRL writing strategies. After signing the online consent form, the participants
provided their responses to these scales online, specifically through an online survey website named Wenjuanxing in China, which shares common features with Qualtrics. Before responding, they were told that their responses might neither be scored nor exert any impact on their academic performance. If they could respond truthfully according to their actual situation, their responses might be highly valued. We took note of and responded to both of their queries and comments concerning but not limited to responding to these scales throughout the whole session. They completed the items of these scales within 4–7 min.
In session two, the participants were given 40 min to complete the assigned argumentative writing task, and then their written essays were assembled to assess their EFL writing achievement. Two experienced CET4 writing raters were invited to score the gathered essays using Jacobs et al.’s grading criteria mentioned above. The raters’ inter-rater reliability was rAB =0.939, indicating reliable scoring.
3.4. Data analysis
Before any analysis was done on the assembled data, the data screening and cleaning recommended by D¨ornyei and Taguchi (2010) were conducted. Five responses were removed from the data because they showed a lack of effort, intentional wrongdoing, or inac- curate answers. Meanwhile, the missing data were also inspected and examined using SPSS 25. Therefore, the data concerning five participants were eliminated from the current study, leaving the finalist list of 391 participants for the analysis. The multivariate normality of the involved variables was examined by Mardia’s skewness and kurtosis test, the results of which indicated that the variables were multivariate non-normally distributed (Mardia’s skewness =239.477 (χ2 =15732.526) >1.96; Mardia’s kurtosis = 1560.288 (χ2 =4515.724) >1.96)
LPAs were employed to explore L2 students’ writing self-efficacy profiles with Mplus 8.3 (Muthen & Muthen, 2017), where we used the maximum likelihood estimation with robust standard errors to estimate the parameters of these latent profile solutions.
Furthermore, whether the constructed latent profiles fitted well with the collected data would be determined based on the estimates proposed by Jung and Wickrama (2008): the Akaike information criterion (AIC), the Bayes Information criterion (BIC), the adjusted Bayes Information criterion (aBIC), and entropy. Besides, the Lo–Mendell–Rubin likelihood ratio test (LMR-LRT) and the bootstrapped likelihood ratio test (BLRT) were employed to detect whether there are significant differences between k and k− 1 profiles.
Moreover, Levene’s Test for Equality of Variance were employed to examine the equality of variance of the collected data before conducting the statistical analyses on the differences across the profiles of writing self-efficacy, the results of which might determine the methods to detect these differences, such as ANOVA or Welch’ Test and Least Significant Difference (LSD) analyses or Tamhane’s T2 Tests. ANOVA or Welch’s Tests were employed to inspect the differences across the best-fitted profiles of writing self-efficacy in writing self-efficacy and SRL writing strategies, and post hoc LSD analyses or Tamhane’s T2 Tests were used to detect the differences between each profile pair. Furthermore, the same statistical methods were also utilized to examine the difference across these profiles in L2 students’ writing achievements. In addition, path analyses were employed to scrutinize the differences across these profiles in the following predictive effects: the predictive effects of writing self-efficacy on SRL writing strategies and the predictive effects of writing self-efficacy and SRL writing strategies on writing achievement. The models constructed for the path analyses were assessed according to the goodness-of-fit indices suggested by Kline (2016): the χ2 test statistic with its level of significance, the comparative fit index (CFI), the Tucker-Lewis index (TLI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR).
4. Results
4.1. Profiles of L2 students’ writing self-efficacy
The profiles of L2 students’ writing self-efficacy were determined using the exploratory LPA. Three solutions, specifically the two- profile, three-profile, and four-profile, were examined. The latent profile that fits the data the best was selected according to all of the aforementioned estimations, specifically, higher entropy, lower AIC, BIC, and aBIC, and the significant p values for LMR and BLRT. The identified profiles could also be checked to see if they differ conceptually from one another in terms of the indicator variables. The LPA results concerning three solutions are presented in Table 1.
As Table 1 shows, compared with the four-profile solution with an insignificant p-value for LMR (pprofile-4 =0.2648), the p-values for the two-profile and three-profile solutions were significant (pprofile-2 =0.0004, pprofile-3 =0.0050). These findings might point to statistical discrepancies between the comparisons of one profile versus two profiles and two profiles versus three profiles rather than three profiles versus four profiles. Meanwhile, in contrast to the entropies of the two-profile and four-profile solutions, the three-profile solution showed a greater entropy value (entropyprofile-3 =0.919 >entropyprofile-2 =0.891>entropyprofile-4 =0.813), suggesting that the three-profile solution might be the best-fitted one for the profiles of L2 students’ writing self-efficacy. The subtlety of the three Table 1
The LPA results.
C K AIC BIC aBIC Entropy LMR BLRT
2 49 19461.677 19656.144 19500.660 .891 .0004 .0000
3 66 18785.451 19047.386 18837.971 .919 .0050 .0000
4 83 18613.804 18943.207 18678.852 .886 .2648 .0000
profiles is displayed in Fig. 1.
According to Fig. 1, participants assigned to Profile 1 were typified by low scores on all the indicator items concerning linguistic self-efficacy, self-regulatory self-efficacy, classroom performance self-efficacy, and genre-based performance self-efficacy; those assigned to Profile 2 by average scores on all the indicator items; those assigned to Profile 3 by high scores on all the items. Overall, the participants in the three profiles scored relatively lower on classroom performance self-efficacy than on linguistic self-efficacy, self- regulatory self-efficacy, and genre-based performance self-efficacy. We labelled Profiles 1 to 3 as “Low on All Self-efficacy”, “Average on All Self-Efficacy”, And “High on All Self-Efficacy”, respectively (see Fig. 2).
ANOVA (or Welch’s Tests) and post hoc LSD analyses (or Tamhane’s T2 Tests) were utilized to detect the differences across three identified profiles in four dimensions of writing self-efficacy. The results of ANOVA (or Welch’s Tests) in Table 2 reveal that three profiles were significantly distinctive in linguistic self-efficacy (F =130.166, p =.000, η2 =0.461), self-regulatory self-efficacy (F = 123.688, p =.000, η2 =0.423), classroom performance self-efficacy (F =246.889, p =.000, η2 =0.56), and genre-based performance self-efficacy (F =314.519, p =.000, η2 =0.67). According to the results of LSD analyses (or Tamhane’s T2 Tests) in Table 3, all profile pairs were significantly different in linguistic self-efficacy (pprofile1-2 =.018, pprofile1-3 =.003), self-regulatory self-efficacy (pprofile1-2 = .000, pprofile1-3 =.000, pprofile2-3 =.000), class performance self-efficacy (pprofile1-2 =.000, pprofile1-3 =.000, pprofile2-3 =.000), and genre- based performance self-efficacy (pprofile1-2 =.000, pprofile1-3 =.000, pprofile2-3 =.000) except the following single pair: the Profile 2 versus Profile 3 pair in linguistic self-efficacy (p =.242).
In addition, the participants in our samples across three profiles were distributed as follows: sixty-seven participants were allocated to Profile 1, accounting for 17.135%; two hundred twenty-five to Profile 2, accounting for 57.545%; and ninety-nine students to Profile 3, accounting for 25.320%.
4.2. Profile differences in SRL writing strategies and writing achievement
In the same vein, ANOVA (or Welch’s Tests) and LSD analyses (or Tamhane’s T2 Tests) were conducted to discover L2 students’
differences in SRL writing strategies and writing achievement across the identified latent profiles of writing self-efficacy, the results of which are shown in Tables 4 and 5.
According to the results of ANOVA (or Welch’s Tests) in Table 4, participants assigned to three profiles were significantly different in the following SRL writing strategies: text revising (F =31.654, p =.000, η2 =0.14), peer learning (F =24.758, p =.000, η2 =0.113), feedback handling (F =6.088, p =.003, η2 =0.028), interest enhancement (F =23.35, p =.000, η2 =0.107), and motivational self-talk (F =33.587, p =.000, η2 =0.135). Statistically significant differences across the three profiles were also found in L2 students’ writing achievement (F =4.536, p =.011, η2 =0.023).
Furthermore, post hoc LSD analyses (or Tamhane’s T2 Tests) were conducted on SRL writing strategies and writing achievement in order to uncover the subtle differences between the profile pairs, the results of which are presented in Table 5.
As the results of LSD analyses (or Tamhane’s T2 Tests) in Table 5 reveal, all profile pairs were significantly distinctive in the following SRL writing strategies: text revising (pprofile1-2 =.000, pprofile1-3 =.000, pprofile2-3 =.000), peer learning (pprofile1-2 =.000, pprofile1-3 =.000, pprofile2-3 =.000), feedback handling (pprofile1-2 =.021, pprofile1-3 =.000, pprofile2-3 =.000), interest enhancement (pprofile1- 3 =.003, pprofile2-3 =.003) and motivational self-talk (pprofile1-2 =.034, pprofile1-3 =.000, pprofile2-3 =.000) except the following single pair: the Profile 1 versus Profile 2 pair in interest enhancement (p =.430). Moreover, the statistically significant differences in writing achievement were found in the Profile 1 versus Profile 2 pair (pprofile1-2 =.018) and the Profile 1 versus Profile 3 pair (pprofile1-3 =0.003)
Fig. 1.Three profiles of writing self-efficacy.
Note: LS =linguistic self-efficacy, SRS =self-regulatory self-efficacy, CPS =classroom performance self-efficacy, and GPS =genre-based perfor- mance self-efficacy.
Fig. 2.Path models of writing self-efficacy on SRL writing strategies.
rather than the Profile 2 versus Profile 3 pair (pprofile2-3 =.242).
4.3. Profile differences in the predictive effects of writing self-efficacy on SRL writing strategies
Path analyses were utilized to explore the predictive effects of L2 students’ writing self-efficacy on their use of SRL writing stra- tegies. Table 6 provides the details about the model fit indices of the models for path analyses, showing that these models are fully saturated. The results of the path analyses are displayed in Table 7.
According to the results of path analyses in Table 7, in terms of text revising, linguistic self-efficacy and self-regulatory self-efficacy demonstrated significant predictive effects for all the participants (βLS =0.247, p =.000; βSRS =0.244, p =.000); no facets of writing self-efficacy exerted significant predictive effects for those in Profile 1; linguistic self-efficacy and self-regulatory self-efficacy also showed significant predictive effects for those in Profile 2 (βLS =0.284, p =.001; βSRS =0.185, p =.015); self-regulatory self-efficacy also showed significant predictive effects for those in Profile 3 (β =0.28, p =.01). In terms of peer learning, self-regulatory self-efficacy and classroom performance self-efficacy demonstrated significant predictive effects for all the participants (βSRS =0.263, p =.000;
βCPS =.21, p =.002); self-regulatory self-efficacy also showed a significant predictive effect for those in Profile 1 (β =0.34, p =.001);
self-regulatory self-efficacy and classroom performance self-efficacy also showed significant predictive effects for those in Profile 2 (βSRS =0.196, p =.01; βCPS =.174, p =.01); no facets of writing self-efficacy exerted significant predictive effects for those in Profile 3.
In terms of feedback handling, linguistic self-efficacy, self-regulatory self-efficacy, and classroom performance self-efficacy demon- strated significant predictive effects for all the participants (βLS =0.135, p =.034; βSRS =0.375, p =.000; βCPS = − 0.312, p =.002);
Table 2
Results of differences in writing self-efficacy across latent profiles.
Factors Mean Levene (df) F Sig. η2
Profile1 Profile2 Profile3
LS 2.949 4.183 5.226 7.703 (2, 388) Welch’s F (2142.796) =130.166 p <.001 .461
SRS 3.453 4.317 5.394 3.504 (2, 388) Welch’s F (2148.240) =123.688 p <.001 .423
CPS 2.168 3.287 4.871 1.144 (2, 388) ANOVA F (2,388) =246.889 p <.001 .56
GPS 2.605 4.080 5.308 8.515 (2, 388) Welch’s F (2143.840) =314.519 p <.001 .67
Note. LS =linguistic self-efficacy, SRS =self-regulatory self-efficacy, CPS =classroom performance self-efficacy, GPS =genre-based self-efficacy.
Table 3
Results of differences in writing self-efficacy across latent profiles.
Factors Profile pair Statistics
MD SE Sig.
LS 1–2 −2.267 .958 .018
1–3 −3.240 1.089 .003
2–3 −.972 .830 .242
SRS 1–2 −1.234 .110 .000
1–3 −2.277 .125 .000
2–3 −1.043 .096 .000
CPS 1–2 −.864 .104 .000
1–3 −1.941 .118 .000
2–3 −1.077 .090 .000
GPS 1–2 −1.119 .111 .000
1–3 −2.703 .126 .000
2–3 −1.585 .096 .000
Note. LS =linguistic self-efficacy, SRS =self-regulatory self-efficacy, CPS =classroom performance self-efficacy, GPS =genre-based self-efficacy.
Table 4
Results of differences in SRL writing strategies and writing achievement across latent profiles.
Factors Mean Levene (df) F Sig. η2
Profile1 Profile2 Profile3
TR 4.202 4.807 5.429 1.814 (2, 388) ANOVA F (2,388) =31.654 p <.001 .14
PL 3.846 4.161 4.845 1.173 (2, 388) ANOVA F (2,388) =24.758 p <.001 .113
FH 5.169 5.277 5.626 2.109 (2, 388) Welch’s F (2155.264) =6.088 p <.005 .028
IE 4.418 4.736 5.475 1.656 (2, 388) ANOVA F (2,388) =23.35 p <.001 .107
MST 4.549 4.998 5.631 3.248 (2, 388) Welch’s F (2157.733) =33.587 p <.001 .135
WA 73.993 76.260 77.232 .466 (2, 388) ANOVA F (2,388) =4.536 p <.05 .023
Note. TR =text revising, PL =peer learning, FH =feedback handling, IE =interest enhancement, MST =motivational self-talk, WA =writing achievement.
Table 5
Results of differences in SRL writing strategies and writing achievement across latent profiles.
Factors Profile pair Statistics
MD SE Sig.
TR 1–2 −1.476 .085 .000
1–3 −2.704 .097 .000
2–3 −1.228 .074 .000
PL 1–2 −.605 .137 .000
1–3 −1.228 .156 .000
2–3 −.623 .119 .000
FH 1–2 −.316 .136 .021
1–3 −.999 .155 .000
2–3 −.684 .118 .000
IE 1–2 −.108 .136 .430
1–3 −.457 .155 .003
2–3 −.349 .118 .003
MST 1–2 −.318 .150 .034
1–3 −1.057 .170 .000
2–3 −.738 .130 .000
WA 1–2 −2.267 .958 .018
1–3 −3.240 1.089 .003
2–3 −.972 .830 .242
Note. TR =text revising, PL =peer learning, FH =feedback handling, IE =interest enhancement, MST =motivational self-talk, WA =writing achievement.
Table 6
Model fit indices for the models for path analyses.
χ2 df CFI TLI RMSEA SRMR AIC BIC
TR 0 0 1 1 0 0 1078.585 1150.022
PL 0 0 1 1 0 0 1090.945 1162.382
FH 0 0 1 1 0 0 1067.462 1138.898
IE 0 0 1 1 0 0 1155.886 1227.322
MST 0 0 1 1 0 0 980.015 1051.452
Overall 0 0 1 1 0 0 4989.702 5148.45
Note. TR =text revising, PL =peer learning, FH =feedback handling, IE =interest enhancement, MST =motivational self-talk.
Table 7
Differences in predictiveness of writing self-efficacy on SRL writing strategies.
Factors overall Profile 1 Profile 2 Profile 3
β p β p β p β p
TR LS .247 .000 .089 .533 .284 .001 .113 .337
SRS .244 .000 .119 .364 .185 .015 .28 .01
CPS − .087 .129 .032 .777 − .121 .072 .016 .876
GPS .13 .052 .189 .101 .082 .381 −.127 .18
PL LS .053 .381 .004 .976 .094 .223 .012 .932
SRS .263 .000 .34 .001 .196 .01 .096 .392
CPS .21 .002 −.055 .672 .174 .01 .222 .076
GPS − .054 .440 −.012 .917 − .026 .778 −.088 .521
FH LS .135 .034 .067 .623 .145 .104 .041 .771
SRS .375 .000 .427 .000 .198 .003 .338 .001
CPS − .209 .004 −.312 .002 − .097 .163 −.209 .016
GPS − .041 .555 −.002 .991 − .043 .651 −.045 .66
IE LS .04 .464 .051 .663 − .01 .895 .113 .393
SRS .371 .000 .196 .122 .343 .000 .253 .022
CPS .081 .186 −.006 .951 .057 .343 .081 .43
GPS − .024 .723 −.015 .904 .032 .736 −.123 .327
MST LS .102 .052 .016 .906 .108 .086 .182 .144
SRS .472 .000 .462 .000 .419 .000 .241 .024
CPS − .038 .526 −.086 .42 − .016 .815 .111 .195
GPS .04 .520 −.045 .721 .089 .313 .035 .756
Note. LS =linguistic self-efficacy, SRS =self-regulatory self-efficacy, CPS =classroom performance self-efficacy, GPS =genre-based self-efficacy, TR
=text revising, PL =peer learning, FH =feedback handling, IE =interest enhancement, MST =motivational self-talk.
self-regulatory self-efficacy and classroom performance self-efficacy also showed a significant predictive effect for those in Profile 1 (βSRS =0.427, p =.000; βCPS = −0.312, p =.002); self-regulatory self-efficacy also showed significant predictive effects for those in Profile 2 (β =0.198, p =.003); self-regulatory self-efficacy and classroom performance self-efficacy exerted significant predictive effects for those in Profile 3 (βSRS =0.338, p =.001; βCPS = − 0.209, p =.016). In terms of interest enhancement, self-regulatory self- efficacy showed significant predictive effects for all the participants (β =0.371, p =.003), those in Profile 2 (β =0.343, p =.000) and those in Profile 3 (β =0.253, p =.022) rather than those in Profile 1. In terms of motivational self-talk, self-regulatory self-efficacy showed significant predictive effects for all the participants (β =0.472, p =.000) and those in Profiles 1 to 3 (βprofile1 =0.462, p = .000; βprofile2 =0.419, p =.000; βprofile3 =0.241, p =.000).
4.4. Profile differences in predictiveness of writing self-efficacy and SRL writing strategies on writing achievement
Path analyses were utilized to determine the differences across profiles of writing self-efficacy in predictive effects of writing self- efficacy and SRL writing strategies on writing achievements. Table 8 provides the details about the model fit indices of the models for path analyses, showing that these models are fully saturated. The results of the path analyses are displayed in Table 9.
As the results of path analyses in Table 9 show, for the overall participants in the current study, linguistic self-efficacy and genre- based performance self-efficacy exposed a significant predictive effect on writing achievements (βLS =0.133, p =.041; βGPS =.179, p = .013). In contrast, for the participants in Profile 1, motivational self-talk showed a significant negative predictive impact on writing achievements (β = − 0.46, p =.011); for those in Profile 2, genre-based performance self-efficacy could significantly predict writing achievements (β =0.184, p =.036); for those in Profile 3, neither facets of writing self-efficacy nor SRL writing strategies demonstrated significant predictive effect on writing achievements.
5. Discussion
The current study aimed to examine profiles of writing self-efficacy and the heterogeneity of its relationship with SRL writing strategies among L2 students. According to the LPAs, three profiles of writing self-efficacy among the participants in the current study came into sight: “Low on All Self-Efficacy”, “Average on All Self-Efficacy”, and “High on All Self-Efficacy”. Moreover, ANOVA and Welch’s Tests showed that there were significant differences across those identified profiles of writing self-efficacy in terms of writing self-efficacy (i.e., linguistic self-efficacy, self-regulatory self-efficacy, classroom performance self-efficacy, and genre-based perfor- mance self-efficacy), SRL writing strategies (text revising, peer learning, feedback handling, interest enhancement, and motivational self-talk), and writing achievements. Furthermore, the results of the post hoc LSD analyses and Tamhane’s T2 Tests showed that all the profile pairs were significantly distinctive in the aforementioned variables except the following pairs: The Profile 1 versus Profile 2 pair in interest enhancement and the Profile 2 versus Profile 3 pair in writing achievement. Besides, path analyses revealed differences across the profiles of writing self-efficacy in the predictive effect of writing self-efficacy on SRL writing strategies and the effects of writing self-efficacy and SRL writing strategies on writing achievement.
5.1. Profiles of writing self-efficacy
Three profiles of writing self-efficacy emerged among the sample of L2 students involved in the current study: “Low on All Self- Efficacy”, “Average on All Self-Efficacy”, and “High on All Self-Efficacy”. This finding is similar to that of Kim et al. (2015) found three profiles of self-efficacy for learning English among Korean-speaking EFL learners: high, medium, and low self-efficacy. What makes the current study distinct from Kim et al. (2015) is the different conceptualizations of self-efficacy adopted. Kim et al. (2015) adopted the unitary conceptualization of self-efficacy and used the mean score of all the items measuring self-efficacy as the basis of LPAs. In contrast, self-efficacy was treated as a multidimensional construct in the current study, and the LPAs were based on all the items, as illustrated in Fig. 1. Therefore, the findings in the current study revealed that three profiles differed significantly in facets of writing self-efficacy, thus contributing to our elaborate grasp of the heterogeneity of L2 students in writing self-efficacy.
The current study also demonstrated significant differences across the identified profiles of writing self-efficacy in terms of SRL writing strategies and writing achievements. L2 students assigned to the High on All Self-efficacy profile reported higher use of SRL writing strategies than those assigned to the Average on All Self-efficacy profile, followed by those assigned to the Average on All Self- efficacy profile. The finding might imply different patterns of integrating writing self-efficacy and SRL writing strategies among L2 students: The Low on All Self-Efficacy profile +low use of SRL writing strategies, the Average on All Self-Efficacy profile +mediate use of SRL writing strategies, the High on All Self-Efficacy profile +high use of SRL writing strategies. These patterns might suggest a positive correlation between facets of writing self-efficacy and the use of SRL writing strategies.
Theoretically, three profiles of writing self-efficacy found in this study might help us identify previously unrecognized latent subgroups in the population of L2 students, each characterized by distinctive levels of writing self-efficacy (i.e., high, medium, and Table 8
Model fit indices for the models for path analyses.
χ2 df CFI TLI RMSEA SRMR AIC BIC
Profiles 0 0 1 1 0 0 2652.535 2783.052
Overall 0 0 1 1 0 0 2624.378 2668.034
