Abstract
Background:
The coronavirus disease pandemic has caused significant disruption in the field of education, resulting in the need for more online classes and a blended offline and online teaching model. Therefore, understanding what makes this model effective is important. Accordingly, this study explored the structural relationships among academic pressure, independent learning ability, and academic self-efficacy in a blended teaching environment during the pandemic and independent learning ability’s mediating effect on the relationship between academic pressure and academic self-efficacy.
Methods:
Adopting a random sampling method, this study surveyed 761 Chinese college, Shaanxi Province, China in 2022 and university students. Factor analysis, correlation analysis, structural equation modeling, and path analysis were used to analyze the data.
Results:
The results show that the academic pressure faced by Chinese English majors had a significant negative impact on academic self-efficacy (P<0.001). However, academic pressure had no statistical effect on students’ independent learning ability (P=0.317). Moreover, independent learning ability had a significant positive effect on academic self-efficacy (P<0.001) and a mediating effect on the relationship between academic pressure and academic self-efficacy (P=0.032).
Conclusion:
Independent learning ability can directly and indirectly affect academic self-efficacy. Thus, in an online and offline blended teaching model, teachers should guide students regarding self-exploration, communication, and cooperation based on existing knowledge and experience. They should also enable students to improve their learning process and independent learning ability. Various language learning situations should be established for learning English so that by experiencing success and failure, students can ultimately improve their academic self-efficacy.
Keywords: Academic pressure, Academic self-efficacy, Blended teaching, COVID-19, Learning
Introduction
The coronavirus disease (COVID-19) pandemic has led to many changes in our everyday lives. These include not only social, economic, political, and cultural impacts but also significant disruption in the field of education. The biggest educational change has been a shift from the “face-to-face” class environment (the traditional teaching method in which teachers lecture and students learn and communicate face-to-face) to “non-face-to-face online classes” (1). Like many other countries during the pandemic, China’s Ministry of Education, following the advice of the Center for Epidemic Prevention and Control, implemented the initiative to “suspend classes without stopping teaching or stopping learning.” This prompted 1,454 universities across the country to implement online teaching and 17.75 million college students to participate in online learning (2). Although online courses have the advantages of shorter learning cycles and lower costs while improving learning efficiency through student satisfaction, they also have various problems and limitations that impact student attention and engagement (3). In August 2020, China’s Ministry of Education proposed the “close integration of online and offline education and teaching.” As a result, in the post-epidemic era, an online and offline blended teaching model has become popular; it is now the new norm for the development of teaching in colleges and universities (4). This blended teaching model reduces the disadvantages of a single teaching model by combining online teaching’s quick pace, timeliness, and freedom from time and place constraints with offline teaching, thus creating a strong sense of learning presence, interaction, and experience for students (5).
Naturally, changes in teaching methods require students to make positive adjustments to adapt to new learning methods. In the case of the blended teaching approach, students who are accustomed to offline teaching may face new academic pressures if they do not adapt. It is well-known that academic pressure can be caused by a mismatch between a student’s skills and abilities and the requirements of the environment, or a mismatch between what the student wants and what the environment provides (6). A previous study has shown that academic pressure is a major source of daily pressure among college students (7). Although moderate academic pressure can stimulate students’ learning and play a positive role, excessive academic pressure can negatively affect their studies and their physical and mental health.
Among contemporary Chinese college students, English learning pressure and learning burnout are common, particularly for English majors (8–9). With rapid globalization, society has higher requirements for English proficiency, particularly among English majors, which increases these students’ learning pressure. We believe this topic requires further exploration to gain a better understanding of the current situation of English learning pressure and how to improve English majors’ ability to adapt to such pressure.
When learning online, students must decide when and where to study. Although online students can study anytime and anywhere provided they have access to the Internet, there are limitations to online learning. While teachers can send online messages or notes to encourage students to study more, for students who exhibit slow class progress, their overall learning efficacy still depends on their motivation and willingness and on whether they are participating fully in class and submitting the work. Thus, if students do not have the ability to learn independently, the success and effectiveness of an online course cannot be guaranteed (10). A student’s independent learning ability not only benefits the student in school but also lays the necessary foundation for lifelong learning. In the process of blended teaching, teachers are no longer the only source of knowledge, which makes it particularly important to cultivate college students’ independent learning ability (11).
Independent learning is influenced by both external environmental factors and the student’s personal factors. Controllable personal factors, such as motivation, strategy, and self-efficacy, can be improved through specific efforts, which are key to improving the student’s independent learning abilities (12).
Bandura (13) defines self-efficacy as an individual’s subjective assessment of their ability to achieve their goals. According to different fields, self-efficacy can be divided into general, social, and academic self-efficacy. Academic self-efficacy is when an individual judges their ability in an academic situation. Such self-efficacy is often composed of the following three factors: homework difficulty preference, self-regulation efficacy, and self-confidence. Students with high academic self-efficacy will choose challenging tasks, put in more effort to successfully complete them, and persevere even when faced with difficulties (14). In the process of learning a language, self-efficacy plays an important role in students’ self-confidence, as it is tied to their expectations for the language level they can attain. Related studies have shown there is a significant relationship between self-efficacy and foreign language learning performance, with self-efficacy acting as a significant predictor of foreign language learning achievement (15–16). The stronger the student’s self-efficacy and the higher the goals they set, the stronger the self-regulation efficacy of their learning (17). Self-efficacy also has responsive effects during self-directed learning programs, behavioral performance, and self-reflection stages (18).
Referring to extant studies, we surveyed English majors from select universities in China to explore the structural relationship among academic pressure, independent learning ability, and academic self-efficacy in a blended teaching environment during the pandemic. We also examined the mediating role of independent learning ability in the relationship between academic pressure and academic self-efficacy. Subsequently, we proposed the following hypotheses. H1: The academic pressure faced by Chinese English majors significantly impacts their academic self-efficacy. H2: The academic pressure faced by Chinese English majors significantly impacts their independent learning ability. H3: The self-regulated learning ability of Chinese English majors significantly affects academic self-efficacy. H4: The independent learning ability of Chinese English majors has a mediating effect on the relationship between academic pressure and academic self-efficacy. Our research model is presented in Fig. 1.
Fig. 1:
Research Model
Materials and Methods
Participants
We adopted a random sampling method to identify English majors from select universities in Shaanxi Province, China in 2022. We used stratified sampling based on sex, grade, and other factors. A total of 761 questionnaires were distributed, and 727 questionnaires were returned. The general participant characteristics are shown in Table 1.
Table 1:
General participant characteristics
| Variables | n | % | |
|---|---|---|---|
| Sex | Male | 90 | 12.4 |
| Female | 637 | 87.6 | |
| Grade | Freshman | 390 | 53.6 |
| Sophomore | 163 | 22.4 | |
| Junior | 110 | 15.2 | |
| Senior | 64 | 8.8 | |
| Total | 727 | 100 | |
All participants provided informed consent, and this study design was approved by Xianyang Normal University (No. 2021Y034), China.
Assessments
All scales were measured using Likert’s 5-point method, with 1–5 representing “strongly disagree” to “strongly agree.” To test the reliability and validity of the scale, we used exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). We also used the aggregation validity index reference standards (average variance extracted [AVE]>0.50) and composite reliability ([CR]>0.70) (19). The results are shown in Table 2. Cronbach’s α for all variables was above 0.80, indicating that the internal factors of the latent variable had high consistency and good reliability. Meanwhile, the AVE and CR of the model were above 0.50 and 0.70, respectively, indicating that the research model had good aggregation validity.
Table 2:
Reliability and validity test results
| Variable | Item | Estimate | SMC | Standardized residuals | CR | AVE | Cronbach’s α | |
|---|---|---|---|---|---|---|---|---|
| Academic Pressure | Course pressure | 28 | 0.693 | 0.480 | 0.520 | 0.887 | 0.496 | 0.889 |
| 27 | 0.738 | 0.545 | 0.455 | |||||
| 24 | 0.746 | 0.557 | 0.443 | |||||
| 23 | 0.726 | 0.527 | 0.473 | |||||
| 20 | 0.698 | 0.487 | 0.513 | |||||
| 19 | 0.690 | 0.476 | 0.524 | |||||
| 18 | 0.658 | 0.433 | 0.567 | |||||
| 11 | 0.678 | 0.460 | 0.540 | |||||
| Exam pressure | 21 | 0.715 | 0.511 | 0.489 | 0.841 | 0.470 | 0.836 | |
| 15 | 0.676 | 0.457 | 0.543 | |||||
| 13 | 0.757 | 0.573 | 0.427 | |||||
| 12 | 0.726 | 0.527 | 0.473 | |||||
| 8 | 0.612 | 0.375 | 0.625 | |||||
| 1 | 0.615 | 0.378 | 0.622 | |||||
| Study pressure | 25 | 0.567 | 0.321 | 0.679 | 0.707 | 0.378 | 0.696 | |
| 6 | 0.597 | 0.356 | 0.644 | |||||
| 5 | 0.709 | 0.503 | 0.497 | |||||
| 2 | 0.575 | 0.331 | 0.669 | |||||
| Employment pressure | 26 | 0.514 | 0.264 | 0.736 | 0.632 | 0.375 | 0.596 | |
| 14 | 0.535 | 0.286 | 0.714 | |||||
| 3 | 0.524 | 0.275 | 0.725 | |||||
| Performance pressure | 16 | 0.743 | 0.552 | 0.448 | 0.782 | 0.642 | 0.709 | |
| 7 | 0.740 | 0.548 | 0.452 | |||||
| Independent Learning Ability | Learning continuity | 10 | 0.750 | 0.563 | 0.438 | 0.911 | 0.631 | 0.900 |
| 11 | 0.712 | 0.507 | 0.493 | |||||
| 13 | 0.854 | 0.729 | 0.271 | |||||
| 14 | 0.830 | 0.689 | 0.311 | |||||
| 15 | 0.815 | 0.664 | 0.336 | |||||
| 16 | 0.795 | 0.632 | 0.368 | |||||
| Course management | 1 | 0.718 | 0.516 | 0.484 | 0.920 | 0.699 | 0.899 | |
| 2 | 0.782 | 0.774 | 0.226 | |||||
| 3 | 0.803 | 0.757 | 0.243 | |||||
| 4 | 0.804 | 0.740 | 0.260 | |||||
| 5 | 0.836 | 0.706 | 0.294 | |||||
| Learning resource management | 17 | 0.789 | 0.623 | 0.377 | 0.897 | 0.636 | 0.903 | |
| 18 | 0.829 | 0.687 | 0.313 | |||||
| 19 | 0.816 | 0.666 | 0.334 | |||||
| 20 | 0.816 | 0.666 | 0.334 | |||||
| 21 | 0.733 | 0.733 | 0.463 | |||||
| Learning motivation | 7 | 0.783 | 0.613 | 0.387 | 0.786 | 0.552 | 0.771 | |
| 8 | 0.801 | 0.642 | 0.358 | |||||
| 9 | 0.634 | 0.402 | 0.598 | |||||
| Academic Self-efficacy | Self-regulation efficacy | 11 | 0.671 | 0.450 | 0.550 | 0.887 | 0.496 | 0.886 |
| 12 | 0.637 | 0.406 | 0.594 | |||||
| 13 | 0.624 | 0.389 | 0.611 | |||||
| 14 | 0.767 | 0.588 | 0.412 | |||||
| 15 | 0.805 | 0.648 | 0.352 | |||||
| 16 | 0.769 | 0.591 | 0.409 | |||||
| 17 | 0.639 | 0.408 | 0.592 | |||||
| 18 | 0.700 | 0.490 | 0.510 | |||||
| Self-confidence | 19 | 0.731 | 0.534 | 0.499 | 0.912 | 0.566 | 0.909 | |
| 20 | 0.842 | 0.709 | 0.291 | |||||
| 21 | 0.820 | 0.672 | 0.328 | |||||
| 22 | 0.790 | 0.624 | 0.376 | |||||
| 23 | 0.802 | 0.643 | 0.357 | |||||
| 24 | 0.697 | 0.486 | 0.514 | |||||
| 25 | 0.631 | 0.398 | 0.602 | |||||
| 26 | 0.677 | 0.458 | 0.542 | |||||
| Homework difficulty preference | 2 | 0.673 | 0.453 | 0.547 | 0.806 | 0.511 | 0.802 | |
| 3 | 0.725 | 0.526 | 0.474 | |||||
| 6 | 0.807 | 0.651 | 0.349 | |||||
| 7 | 0.643 | 0.413 | 0.587 | |||||
Root mean square error of approximation=0.044, Tucker–Lewis index=0.897, comparative fit index=0.905, incremental fit index=0.906, χ2=4218.954 (P<0.001), df=1741, χ2/df=2.423
SMC, squared multiple correlation; CR, composite reliability; AVE, average variance extracted
Academic Pressure
The questionnaire measuring academic pressure among English majors was based on the Academic Stress Scale developed by Oh and Cheon (20). The factors of academic pressure consisted of 5 aspects and 28 questions. Cronbach’s αs for exam, study, employment, and performance pressure (respectively) were 0.836, 0.696, 0.596, and 0.709, respectively.
Independent Learning Ability
The measurement of the Independent Learning Ability Scale was based on Bae and Lee’s (21) questionnaire. Cronbach’s α for each factor of the Independent Learning Ability Scale, as measured by Bang (22), was 0.90 or above, and it had good reliability and validity after the test; we used this in our study after modification and improvement. The scale comprised 21 questions relating to 4 aspects. Among them, Cronbach’s αs for learning continuity, course management, learning resource management, and learning motivation were 0.900, 0.899, 0.903, and 0.771, respectively.
Academic Self-efficacy
Our questionnaire measuring English majors’ academic self-efficacy was based on Kim and Park’s (23) questionnaire, which we modified and advanced in our study. Academic self-efficacy consisted of 26 questions relating to 3 aspects. Among them, Cronbach’s αs for self-regulation efficacy, self-confidence, and homework difficulty preference were 0.886, 0.909, and 0.802, respectively.
Statistical Analysis
We used SPSS 22.0 and AMOS 25.0 (IBM Corp., Armonk, NY, USA) for the data processing and statistical analysis. As stated, our data analysis methods included EFA and CFA, correlation analysis among the variables, structural equation modeling (SEM), path analysis, and bootstrapping mediation detection. After verifying the fit of the structural relationship for each variable in the hypothesized model, we analyzed the data. Statistical significance was set at P<0.05.
Results
Correlations among academic pressure, independent learning ability, and academic self-efficacy
The results of our correlation analysis among academic pressure, independent learning ability, and academic self-efficacy are shown in Table 3. There was a negative correlation between learning pressure under the academic stress variable and learning continuity. There was also a negative correlation between course management and learning motivation under the independent learning ability variable (r=0.130–0.211; P<0.01). This implies that the greater the learning pressure faced by these English majors, the weaker their learning continuity, course management, and learning motivation.
Table 3:
Correlations among academic pressure, independent learning ability, and academic self-efficacy
| Variable | 1-1 | 1-2 | 1-3 | 1-4 | 1-5 | 2-1 | 2-2 | 2-3 | 2-4 | 3-1 | 3-2 | 3-3 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1-1 | 1.000 | |||||||||||
| 1-2 | 0.680** | 1.000 | ||||||||||
| 1-3 | 0.515** | 0.511** | 1.000 | |||||||||
| 1-4 | 0.527** | 0.439** | 0.357** | 1.000 | ||||||||
| 1-5 | 0.385** | 0.376** | 0.484** | 0.425** | 1.000 | |||||||
| 2-1 | −0.089* | −0.022 | −0.211** | −0.023 | −0.023 | 1.000 | ||||||
| 2-2 | 0.030 | 0.077* | −0.130** | −0.013 | −0.012 | 0.696** | 1.000 | |||||
| 2-3 | 0.168** | 0.165** | −0.061 | 0.091* | −0.044 | 0.646** | 0.602** | 1.000 | ||||
| 2-4 | 0.091* | 0.037 | −0.173** | 0.088* | −0.024 | 0.659** | 0.635** | 0.601** | 1.000 | |||
| 3-1 | −0.118** | −0.025 | −0.163** | −0.002 | 0.030 | 0.698** | 0.626** | 0.511** | 0.524** | 1.000 | ||
| 3-2 | 0.615** | 0.529** | 0.274** | 0.384** | 0.261** | 0.032 | 0.107** | 0.240** | 0.120** | 0.017 | 1.000 | |
| 3-3 | 0.371** | 0.338** | 0.258** | 0.281** | 0.252** | 0.175** | 0.204** | 0.280** | 0.175** | 0.277** | 0.345** | 1.000 |
1-1. course pressure, 1-2. exam pressure, 1-3. study pressure, 1-4. employment pressure, 1-5. performance pressure, 2-1. learning continuity, 2-2. course management, 2-3. learning resource management, 2-4. learning motivation, 3-1. self-regulation efficacy, 3-2. self-confidence, 3-3. homework difficulty preference
P<0.01,
P<0.05; tested via correlation analysis
Moreover, there was a negative correlation between academic pressure and self-regulation efficacy under the academic self-efficacy variable (r=0.118–0.163; P<0.01). However, there was a positive correlation among independent learning ability, self-regulation efficacy, self-confidence, and homework difficulty preference under the academic self-efficacy variable (r=107–0.698; P<0.01). The implication here is that the stronger the independent learning ability of these English majors, the better their self-regulation ability and self-confidence, and the higher their preference for difficult homework tasks.
Suitability of the Research Model
We established an SEM to explore the relationships among academic pressure, independent learning ability, and academic self-efficacy for English majors in a blended teaching environment during the pandemic. The results show that the research model fit well, with the goodness-of-fit index, incremental fit index, Tucker–Lewis index, and comparative fit index all greater than 0.90 and the root mean square error of approximation<0.100 (Table 4).
Table 4:
Suitability of the research model
| Variable | χ2 | df | GFI | NFI | IFI | TLI | CFI | RMR | RMSEA |
|---|---|---|---|---|---|---|---|---|---|
| Model fit | 303.606 | 46 | 0.933 | 0.926 | 0.937 | 0.909 | 0.937 | 0.020 | 0.088 |
GFI, goodness-of-fit index; NFI, normed fit index; IFI, incremental fit index; TLI, Tucker–Lewis index; CFI, comparative fit index; RMR, root mean square residual; RMSEA, root mean square error of approximation
Model fit cutoff values: RMSEA<0.100, TLI≥0.900, CFI≥0.900
Hypothesis Verification
We analyzed the path relationships among academic pressure, independent learning ability, and academic self-efficacy, as shown in Table 5. Among all the variables, academic pressure had a statistically negative effect on academic self-efficacy (β=−0.183, P<0.001), indicating that academic pressure affected academic self-efficacy. As academic pressure increased, academic self-efficacy decreased. The sense of self-confidence, which affects the impact of independent learning ability on academic self-efficacy (β=0.826, P<0.001), had a statistically positive effect; namely, the stronger the independent learning ability of these students, the stronger their academic self-efficacy and self-confidence.
Table 5:
Path relationships among academic pressure, independent learning ability, and academic self-efficacy
| Hypotheses | Path | β | Standard error | Critical ratio | Assessment |
|---|---|---|---|---|---|
| H1 | Academic pressure → Academic self-efficacy | −0.183 | 0.046 | −3.999*** (p=0.000) | Accept |
| H2 | Academic pressure → Independent learning ability | 0.061 | 0.061 | 1.001 (p=0.317) | Reject |
| H3 | Independent learning ability → Academic self-efficacy | 0.826 | 0.034 | 24.068*** (p=0.000) | Accept |
P<0.001,
P<0.01,
P<0.05; tested by path analysis
Mediating effect of independent learning ability between academic pressure and academic self-efficacy
Based on the research model, to test the mediating effect of independent learning ability between academic pressure and academic self-efficacy, we used the bootstrapping mediating effect, setting the confidence interval at 95%. The results shown in Table 6 indicate that the upper and lower limits of the indirect effect of academic pressure on academic self-efficacy were not included in the confidence interval 0.
Table 6:
Direct, indirect, and total effects
| Path of influence | Direct effect | Indirect effect | Total effect |
|---|---|---|---|
| Academic pressure → academic self-efficacy | −0.117*** | 0.032 | −0.084*** |
| Academic pressure → independent learning ability | 0.043 | - | 0.043 |
| Independent learning ability → academic self-efficacy | 0.755*** | - | 0.755*** |
P<0.05,
P<0.01; tested by the bootstrap method
Discussion
This study aimed to clarify the relationships among academic pressure, independent learning ability, and academic self-efficacy in a blended teaching environment during the COVID-19 pandemic by examining Chinese English majors. We also tested the mediating role of independent learning ability in the relationship between academic pressure and academic self-efficacy. The following discussion is based on our results.
Regarding H1, the academic pressure faced by Chinese English majors has a statistically significant negative impact on their academic self-efficacy: the greater the academic pressure faced by these students, the lower their academic self-efficacy. These results are consistent with Kouzma and Kennedy’s results (24), confirming that the rate of change of academic pressure has a significant negative impact on the rate of change of self-efficacy. Thus, knowing how to deal with academic pressure can restore students’ self-efficacy, which has significant implications for their personal development during their school years. Wu’s (25) research also shows that academic emotions are closely related to self-efficacy. Excessive learning pressure and high academic self-efficacy are regarded as two separate poles. When individuals have excessive learning pressure, their academic self-efficacy will be low; when learning pressure is low, academic self-efficacy is relatively high. The results also show that the degree of learning pressure impacts academic self-efficacy. Students with high self-efficacy usually have higher self-regulation ability and self-judgment in terms of their ability to successfully complete their studies and tasks. As such, these students are more confident when encountering difficulties and setbacks in their studies and daily lives, and their academic pressure is relatively low. Students with low self-efficacy tend to have low self-esteem and other negative emotions. These students have low self-evaluations of their studies and are more likely to feel pressure.
Regarding H2, academic pressure has no statistical effect on independent learning ability. However, the results of the correlation analysis for each sub-variable show that there is a negative correlation between learning pressure under the academic pressure variable and one for learning continuity, course management, and learning motivation under the independent learning ability variable. Thus, the greater the learning pressure faced by English majors, the weaker their learning continuity and learning motivation and the worse their course management. Lee and Bak (26) research results show that academic pressure and independent learning ability among nursing students are negatively correlated—the implication being the need to reduce the academic pressure students experience to improve their independent learning ability.
Regarding H3, independent learning ability has a statistically positive impact on academic self-efficacy: The stronger the independent learning ability of students, the better they are at choosing proper resources, using the right strategies, and employing reasonable practices. In the process of online learning, students can make full use of the resources provided by their teachers and classmates and can pay better attention to the internalization and externalization of knowledge, thereby improving the reciprocity and transformative power of learning (27) as well as their sense of academic self-efficacy. Li and Yang’s (28) results show that learners with strong independent learning ability are more likely to achieve better learning effects through in-depth online learning interaction. Specifically, the multi-interactive experience between learners (the perceptual experience of sharing, discussing, cooperating, and receiving feedback among learners and peers) significantly impacts in-depth learning. Some researchers (20) believe students develop their sense of self-efficacy gradually in the process of learning independently, and that those with a higher level of independent learning ability have a higher sense of self-efficacy.
Regarding H4, independent learning ability shows a significant mediating effect in the impact of academic pressure on academic self-efficacy: To an extent, the impact of academic pressure on academic self-efficacy is realized through the mediating mechanism of the student’s independent learning ability. This finding shows that the academic pressure faced by English majors not only directly but also indirectly affects their academic self-efficacy by affecting their independent learning ability. Relevant research results show that appropriate emotions and pressure are also important factors that affect learning autonomy, student goal setting, and academic self-efficacy (27), and that academic pressure has both negative and positive effects. According to the Yerkes-Dodson law (29), moderate academic pressure positively affects academics; namely, student adjustment to the degree of academic pressure can positively impact their academic and nonacademic activities. Appropriate learning pressure can make students believe they can solve learning difficulties and achieve learning goals through hard work and by adjusting their learning process, which affects their own learning initiatives and their sense of efficacy.
Conclusion
The academic pressure of English majors had a statistically significant negative impact on their academic self-efficacy, indicating that the greater their academic pressure, the lower their academic self-efficacy. Their independent learning ability had a statistically positive impact on academic self-efficacy. Moreover, their independent learning ability had a mediating effect on the relationship between academic pressure and academic self-efficacy; that is, the impact of academic pressure on academic self-efficacy was also realized to an extent through the intermediary mechanism of independent learning ability.
Journalism Ethics considerations
Ethical issues (Including plagiarism, informed consent, misconduct, data fabrication and/or falsification, double publication and/or submission, redundancy, etc.) have been completely observed by the authors.
Acknowledgements
This research received external funding of Shaanxi Institute of Education as one of the research findings of 2021 Education and Teaching Reform Research Project (No. 2021Y034).
Footnotes
Declaration Statement
The author declares no conflicts of interest.
References
- 1.Byun JH, Jee MJ. (2021). EFL learners’perspectives toward live-session based elective English course in COVID-19 environment. Modern Studies in English Language & Literature, 65(2):113–135. [Google Scholar]
- 2.Ministry of Education of the People's Republic of China (2020). The situation of online education in colleges and universities and considerations for the next step. Ministry of Education of the People's Republic of China. [Google Scholar]
- 3.Do JW. (2020). An investigation of design constraints in the process of converting face-to-face course into online course. Journal of Education & Culture, 26(2):153–173. [Google Scholar]
- 4.Ministry of Education of the People's Republic of China (2020). Continue to promote the integration of online and offline teaching in the new semester. Ministry of Education of the People's Republic of China. [Google Scholar]
- 5.Jiang YH, Chen YY, Lu JS, et al. (2021). The effect of the online and offline blended teaching mode on English as a foreign language learners’ listening performance in a Chinese context. Front Psychol, 12:742742. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Kim BH, Jeong MA, Kim EJ. (2021). Satisfaction and effectiveness of online classes of college students in COVID-19. The Journal of Learner-Centered Curriculum and Instruction, 21(5):767–780. [Google Scholar]
- 7.Barbayannis G, Bandari M, Zheng X, et al. (2022). Academic stress and mental well-being in college students: correlations, affected groups, and COVID-19. Front Psychol, 13:886344. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Syed NB. (2021). Impact of levels of education on perceived academic stress and mental wellbeing: an investigation into online mode of learning during pandemic. Journal of Psychological Research. 3:12–18. [Google Scholar]
- 9.Cai WQ. (2022). The status quo of college students' participation in English online learning in the blended learning environment and the ways to improve it. J Environ Public Health, 2022:7255034. [DOI] [PMC free article] [PubMed] [Google Scholar] [Retracted]
- 10.Bandura A. (1997). Self-efficacy: The exercise of control. WH Freeman/Times Books/Henry Holt & Co., New York, USA. [Google Scholar]
- 11.Sun T, Wang C. (2020). College students writing self-efficacy and writing self-regulated learning strategies in learning English as a foreign language. System, 90:102221, [Google Scholar]
- 12.Jeong AL. (2012). Mediating effect of outcome expectations in the relationship of academic self-efficacy to major adjustment of college students. Korean Journal of Counseling, 13(5):2329–2344. [Google Scholar]
- 13.Bandura A. (1986). Social foundations of thought and action: a social cognitive theory. Englewood Cliffs, NJ: Prentice Hall. USA. [Google Scholar]
- 14.Omer O. (2021). Examining the roles of self-efficacy beliefs, self-regulated learning and foreign language anxiety in the academic achievement of tertiary EFL learners. Participatory Educational Research. 8(2):357–372. [Google Scholar]
- 15.George JM, Jones GR. (2012). Understanding and managing organizational behavior (6th ed). Prentice Hall, New Jersey, USA. [Google Scholar]
- 16.Raoofi S, Tan BH, Chan SH. (2012). Self-efficacy in second/foreign language learning contexts. English Language Teaching, 5(11):60–73. [Google Scholar]
- 17.Schunk DH. (1990). Goal setting and self-efficacy during self-regulated learning. Educ Psychol, 25(1):71–86. [Google Scholar]
- 18.Schunk D, Ertmer PA. (2000). Self-regulation and academic learning: self-efficacy enhancing interventions. Academic Press, San Diego, USA. [Google Scholar]
- 19.Kline RB. (2015). Principles and practice of structural equation modeling (4th ed.). The Guilford Press, New York, USA. [Google Scholar]
- 20.Oh MH, Cheon SM. (1994). Analysis of academic stressors and symptoms of juveniles and effects of mediation training on academic stress reduction. Human Understanding, 15(6):63–96. [Google Scholar]
- 21.Bae EK, Lee MY. (2010). The development of the self-directed learning ability inventory for employees in HRD companies. The Korean Journal of Human Resource Development, 12(3):1–26. [Google Scholar]
- 22.Bang HW. (2018). The structural relationships among achievement motivation, academic emotion regulation, self-directed learning ability and learning flow of university student. Journal of Learner-Centered Curriculum and Instruction, 19(12):1213–1239. [Google Scholar]
- 23.Kim AY, Park IY. (2001). Construction and validation of academic self-efficacy scale. The Journal of Educational Research, 39(1):95–123. [Google Scholar]
- 24.Kouzma NM, Kennedy GA. (2000). Academic stress, self-efficacy, social support, and health behaviours in female Victorian Certificate of Education (VCE) students. Educ Dev Psychol, 2(17):24–43. [Google Scholar]
- 25.Wu XX. (2018). A study of the relationship between migrant children’s self-efficacy and social anxiety and group counseling. Master's thesis. Wuhan: Central China Normal University, China. [Google Scholar]
- 26.Lee YM, Bak BG. (2019). Longitudinal analysis of the relationship among academic stress, self-efficacy and academic achievement of the early youth in Gyeonggi Province. The Korean Journal of Thinking Development, 15(2):1–25. [Google Scholar]
- 27.Zhan MG. (2020). An empirical analysis of college students' autonomous learning ability and self-efficacy. Journal of Chongqing University of Science and Technology (Social Sciences Edition), 3:105–108. [Google Scholar]
- 28.Li J, Yang W. (2020). Research on the influence of academic self-efficacy, internal control personality and mindfulness on self-directed learning ability. Mental Health Education, 12(4):140–143. [Google Scholar]
- 29.Yeskes RM, Dodson JD. (1908). The relation of strength of stimulus to rapidity of habit-formation. J Comp Neurol, 18:459–482. [Google Scholar]