Introduction
With the reform of various online teaching modes and the multi-spatial changes in the student learning environment, the nursing field has increasingly emphasized the need for nursing students to employ metacognitive awareness in developing their professional knowledge and practical skills [1]. Recent research indicates that heightened metacognitive awareness is associated with improved patient care [2], clinical reasoning [3], clinical decision-making [4], and the promotion of continuous lifelong learning [5]. These aspects are vital to cultivate in nursing practice. Metacognitive awareness is considered the core component of thinking activities [6] and a crucial element contributing to students’ academic success [7].
Literature review
Metacognitive awareness encompasses knowledge of cognition and the regulation of cognition, defined as regulating one’s knowledge, cognitive processes, and understanding of one’s feelings about these processes [8]. Knowledge of cognition involves declarative knowledge, procedural knowledge, and conceptual knowledge. Regulation of cognition requires planning, monitoring, information management strategies, debugging strategies, and evaluation [9]. Metacognitive awareness draws from Zimmerman’s three-stage cycle theory and Bandura’s self-regulation theory [10, 11]. These theories underscore that metacognitive awareness is constructed through an interactive process involving a learner’s environment, individual abilities, and behavior, as depicted in Fig. 1 [11]. Previous studies have indicated a positive relationship between individual skills such as learning motivation, emotional intelligence, and metacognitive awareness [12, 13]. In addition, self-directed learning ability has been shown to enhance metacognitive awareness [14]. However, the innovative development of digital education has diversified the needs of nursing students’ educational environment, prompting attention to the mechanisms influencing students’ metacognitive awareness [15]. Therefore, it is crucial to investigate the complex influencing mechanisms of environmental factors (educational environment), individual factors (learning motivation, emotional intelligence), and behavioral factors (self-directed learning ability) in nursing students’ metacognitive awareness.
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Fig. 1
A Theoretical framework of the interaction of individual, environmental, and behavioral factors on metacognitive awareness
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Educational environment refers to subjective and objective factors that are directly or indirectly related to “teaching” and “learning” [16]. The nursing educational environment includes subjective and objective factors directly or indirectly related to learning, teachers, academics, the environment, and social interaction in school or practice settings [17]. A positive subjective experience with the educational environment enhances students’ intrinsic interest in learning and boosts their motivation. Conversely, low satisfaction diminishes enthusiasm, decreasing learning effectiveness [18]. The educational environment significantly influences the development of metacognitive knowledge in medical students, indirectly shaping metacognitive consciousness and playing a crucial role in enhancing various student abilities [19]. It promotes improving students’ self-directed learning ability and positively contributes to constructing emotional intelligence [20, 21].
Learning motivation represents students’ dynamic inclination to initiate and sustain learning behavior to achieve specific academic goals [22]. Intrinsic motivation stems from internal needs, such as a student’s internal drive, curiosity, interest, and desire to enhance their abilities, which encourage active studying [23, 24]. On the other hand, extrinsic motivation results from external incentives, such as students seeking difficult-to-obtain rewards or avoiding punishment from teachers or parents [25]. A previous study demonstrated the significant impact of learning motivation on learning behavior, indicating that internal motivation can enhance self-directed learning ability [26]. External learning motivation influences the development of students’ knowledge about cognition and the regulation of cognition [27]. However, how learning motivation affects metacognitive awareness and its mechanism has not been explored yet [23]. Thus, understanding the relationship between learning motivation, self-directed learning ability, and metacognitive awareness is crucial.
Emotional intelligence combines “interrelated emotional and social competencies, skills, and facilitators that determine how effectively we understand and express ourselves, understand others and relate to them, and cope with daily demands” [28]. This attribute plays a crucial role in healthcare and healthcare education, particularly in the context of hospital nursing training [29]. Nurses with high emotional intelligence can navigate social interactions through emotional regulation, directly influencing their personal self-expression and work behavior [22]. A prior study discovered a positive correlation between students’ emotional intelligence, learning motivation, and its positive impact on reading comprehension and language learning strategies [30]. Emotional intelligence correlates significantly and positively with self-directed learning ability [31]. Within emotional intelligence, metacognitive learning strategies are employed, generated by deep, flexible, and complex self-regulation to enhance individual learning [32]. Therefore, emotional intelligence is closely linked to students’ self-directed learning ability and metacognitive awareness. Despite this, there is a gap in research concerning the relationship between nursing students’ emotional intelligence, self-directed learning ability, and metacognitive awareness.
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Self-directed learning ability (SDLA) is learners’ capacity to independently assess their learning needs, set learning goals, identify human and material resources, choose and implement appropriate learning strategies, and evaluate outcomes, either with or without external assistance [33]. SDLA is pivotal in nurturing collaborative skills essential for multidisciplinary practice in contemporary healthcare settings [34]. Utilizing SDLA contributes to the development of metacognitive insights and enhances the application of metacognition in clinical reasoning contexts [35]. Previous reports have indicated that the decision-making and control processes inherent in SDLA are linked to cognitive improvement following self-directed coding, the learner’s current learning state, environmental information acquisition, and internal learner needs [36]. However, these relationships’ specific mechanisms and action pathways are yet to be thoroughly explored.
Building on our literature review and theoretical framework, this study aimed to (i) explore statistical differences in nursing students’ metacognitive awareness, educational environment, learning motivation, emotional intelligence, and self-directed learning ability based on age, sex, and year level. (ii) investigate the impact of environmental factors (educational environment), individual factors (learning motivation, emotional intelligence), and behavioral factors (self-directed learning ability) on metacognitive awareness using structural equation modeling. The following hypotheses were proposed in this study.
H1: Educational environment positively influences self-directed learning ability.
H2: Learning motivation positively influences self-directed learning ability.
H3: Emotional intelligence positively influences self-directed learning ability.
H4: Self-directed learning ability positively influences metacognitive awareness.
H5: Educational environment positively influences metacognitive awareness.
H6: Self-directed learning ability has been mediates the relationship between the educational environment and metacognitive awareness.
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H7: Self-directed learning ability indirectly influences the associations of learning motivation with metacognitive awareness.
H8: Self-directed learning ability indirectly influences the associations of emotional intelligence with metacognitive awareness.
Methods
Design
Based on the theoretical framework of the direct and possible potential impacts of environment, individual, and behavioral factors on metacognitive awareness in Fig. 1, this study constructs research hypotheses with metacognitive awareness as the dependent variable, educational environment, learning motivation, and emotional intelligence as the independent variables, and self-directed learning ability as the mediator variable. A cross-sectional design was adopted, and data was collected in October 2022.
Sampling and participants
Using stratified quota sampling, 600 nursing students were recruited from two public research universities in Huzhou, Zhejiang Province, China. A hundred students from each university were selected from the first, second, and third academic years. The inclusion criteria were officially enrolled nursing students who provided informed consent. Exclusion criteria included those uninterested in participating and students transitioning from junior college to a bachelor’s degree. For the structural equation model, the sample size was determined by multiplying the total items of the scales by 10 ([N = 3 + 2 + 5 + 2 + 4 + 4] × 10 = 200) [37]. Considering the comparative study of research variables between grades, 200 students were chosen from each grade level, satisfying the required sample size for the SEM analysis and ensuring diversity in the sampling of registered students in the school.
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Instruments
Demographic characteristics
Demographic variables included age, sex (male, female), and year level (first, second, and third year).
Metacognitive Awareness Inventory (MAI)
The MAI includes 52 items and two subscales: metacognitive knowledge and metacognitive regulation [8]. Each item is scored on a 5-point Likert scale, ranging from 1 (never) to 5 (always). Total scores ranged from 52 to 260. The higher the score, the higher the level of metacognitive awareness. MAI has been previously used to assess metacognitive awareness among nursing students in China and has good reliability and validity. Cronbach’s alpha values of the total scale and subscales were 0.96 and 0.80–0.99, respectively [38].
Medical educational environment
The Medical Education Environment Evaluation Scale consists of 50 items and five subcategories: teacher perception, academic perception, learning perception, environment perception, and social self-perception. Each item is scored on a 5-point scale, ranging from 0 (strongly disagree) to 4 (strongly agree), for a total score of 200 points. The full scale and subscales Cronbach’s alpha values were 0.93 and 0.64–0.84, respectively, indicating that the scale has good reliability and validity [39].
Learning motivation
The Learning Motivation Scale consists of 30 items and two subcategories: internal learning motivation and external learning motivation. Each item is scored on a 4-point scale, ranging from 1 (completely inconsistent) to 4 (entirely consistent). Cronbach’s alpha of the full scale and sub-scales was 0.90 and 0.80–0.78, respectively, indicating that the scale has good reliability and validity [40].
Emotional intelligence
The Emotional Intelligence Scale consists of 33 items and four subcategories: emotional perception, emotional expression, self-emotion management, and emotional management of others. Each item is scored on a 5-point scale, ranging from 1 (completely inconsistent) to 5 (entirely consistent). Cronbach’s alpha of the full scale and sub-scales was 0.90 and 0.74–0.89, respectively, indicating that the scale has good reliability and validity [41].
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Self-directed learning ability
The Self-Directed Learning Ability Scale comprises 30 items and four subcategories: learning ability, self-management, learning cooperation, and information quality. Each item is scored on a 5-point scale, ranging from 1 (completely inconsistent) to 5 (entirely consistent). The total score on the scale ranged from 30 to 150 points. The full-scale and sub-scale Cronbach’s alpha values were 0.82 and 0.73–0.86, indicating the scale has reliability and validity [42].
Data collection
After obtaining approval from the administrations of the two universities, six graduate students conducted a face-to-face survey on 600 nursing students from their first, second, and third years. We explained the purpose and method of the study and, after obtaining informed consent, distributed a paper questionnaire that allowed for inquiries about questionable content, and the investigator carefully answered it. Finally, the completeness of the survey questionnaire was checked. The time needed to complete the survey was 25–30 min.
Data analysis
This study used IBM SPSS 21.0 (IBM Corp., Armonk, NY, USA) to analyze the data statistically. Descriptive data for variables are reported using mean and standard deviation (M ± SD). Differences in scale scores of surveyed nursing students with different demographic characteristics were analyzed using independent-sample t-tests and one-way analysis of variance.
In the data analysis, we applied a three-stage approach. First, we constructed a hypothetical structural equation model. Second, we conducted a confirmatory factor analysis to test the measurement model and establish statistically reliable measures of each underlying structure. Third, we analyzed the structural model, specifying the relationships between the underlying structures. Mplus 8.0 was used to analyze the model with a variance-covariance matrix as input.
Moreover, confirmatory factor analysis (CFA) was conducted to test the measurement model. The factor loading for item reliability is higher than the threshold value of 0.60. The scale’s construct reliability (CR) and average variance extracted (AVE) value exceeded the evaluative criteria of 0.70 and 0.50, indicating the scale’s high construct validity and discrimination level. As for the scale’s goodness of fit test, including Chi-square/degrees of freedom (χ2/df), we ideally fall between 1 and 3, showing that the hypothesized model fits well with the sample data. The incremental fit index (IFI) and comparative fit index (GFI) should be greater than or equal to 0.90, which indicates suitable suitability. The root mean square error of approximation (RMSEA) and standardized root mean square residual (SRMR) should be less than 0.06 and 0.08, respectively. Standardized coefficients (β) were examined for regression path analyses [43].
In addition, we employed a bootstrapping procedure to evaluate the mediating effects, with 2,000 iterations and bias-corrected 95% confidence intervals. The bias-corrected 95% confidence interval was used to assess the significance of direct and indirect effects. The effect would be considered significant if the 95% confidence interval did not encompass zero [43].
Results
Demographic information
Six hundred nursing students completed the questionnaire, and the mean age of respondents was 19.99 ± 0.88 years. Self-directed learning ability scores were higher among female students than male students, the difference is statistically significant (p < 0.05). Other variable sex scores showed no significant differences (p > 0.05). Moreover, scores for all study variables were significantly different according to age (p < 0.01). With older age, variable scores were higher. All study variable scores were significantly different according to year level (p < 0.01), with higher scores associated with a higher year level, as shown in Table 1.
Table 1
Analysis of each variable on demographic factors 0f nursing students (n = 600)
Variables | Items | N(%) | Metacognitive awareness | Education environment | Learning motivation | Emotional intelligence | Self-directed learning ability |
|---|---|---|---|---|---|---|---|
Age | A < 20years | 208 (34.7) | 155.42 ± 51.46 | 102.89 ± 22.06 | 63.58 ± 9.06 | 100.94 ± 19.40 | 90.57 ± 18.66 |
A = 20years | 186 (31.0) | 165.34 ± 36.03 | 116.66 ± 18.68 | 70.45 ± 7.70 | 111.55 ± 15.69 | 99.97 ± 14.27 | |
A > 20years | 206 (34.3) | 173.21 ± 34.99 | 122.25 ± 23.25 | 72.40 ± 8.22 | 116.58 ± 18.28 | 104.72 ± 15.16 | |
F p | 9.35 (< 0.001) | 43.48 (< 0.001) | 62.77 (< 0.001) | 40.26 (< 0.001) | 40.22 (< 0.001) | ||
Sex | Male | 56 (9.3) | 142.28 ± 42.54 | 100.27 ± 25.04 | 61.05 ± 8.52 | 94.67 ± 16.73 | 85.77 ± 19.85 |
Female | 544 (90.7) | 167.05 ± 41.38 | 115.40 ± 22.26 | 69.65 ± 8.86 | 111.29 ± 18.57 | 99.78 ± 16.35 | |
T p | 0.41 (0.522) | 0.09 (0.761) | 1.62 (0.204) | 3.06 (0.081) | 4.25 (0.040) | ||
Year level | 1st year | 200 (33.4) | 155.43 ± 51.46 | 102.89 ± 22.06 | 63.58 ± 9.06 | 100.95 ± 19.40 | 90.57 ± 18.66 |
2nd year | 200 (33.3) | 164.39 ± 35.90 | 116.30 ± 18.52 | 70.36 ± 7.61 | 111.43 ± 15.48 | 99.86 ± 14.07 | |
3rd year | 200 (33.3) | 174.40 ± 34.82 | 122.77 ± 23.39 | 72.63 ± 8.29 | 116.86 ± 18.48 | 104.99 ± 15.31 | |
F p | 10.49 (< 0.001) | 44.82 (< 0.001) | 63.70 (< 0.001) | 41.02 (< 0.001) | 41.05 (< 0.001) |
Test of the measurement model
Table 2 shows that all item reliability factor loading values ranged from 0.673 to 0.925, higher than the threshold value of 0.60. The composite reliability values ranged from 0.759 to 0.911, higher than the minimum value of 0.70, and the advertising value equivalent (AVE) values ranged from 0.616 to 0.776, far higher than the threshold value of 0.50. Hence, the measurement model in this study established the convergent validity of all measurement items. In addition, the test result for discriminant validity, which assesses whether individual indicators can adequately distinguish between different constructs, displayed that the square root of AVE for each construct was much higher (0.785–0.881) than the corresponding correlation matrix (0.493–0.729) for that variable in all cases, thereby ensuring discriminant validity.
Table 2
Item reliability, convergent validity, discriminate validity and correlation matrix
Variables | Item | Item reliability | Composite reliability | Convergence validity | Discriminate validity | ||||
|---|---|---|---|---|---|---|---|---|---|
STD. LOADING | CR | AVE | MA | EE | LM | EI | SDLA | ||
MA | 2 | 0.674–0.882 | 0.759 | 0.616 | 0.785a | ||||
EE | 5 | 0.673–0.905 | 0.910 | 0.671 | 0.564** | 0.819a | |||
LM | 2 | 0.835–0.925 | 0.874 | 0.776 | 0.526** | 0.704** | 0.881a | ||
EI | 4 | 0.723–0.921 | 0.906 | 0.709 | 0.667** | 0.653** | 0.708** | 0.842a | |
SDLA | 4 | 0.801–0.868 | 0.911 | 0.719 | 0.493** | 0.729** | 0.643** | 0.634** | 0.848a |
Correlation analysis
All measures had acceptable reliabilities (ranging from 0.673 to 0.925). Pearson correlation matrices for the relationships between variables are displayed in Table 2, indicating significant correlations among the study variables (ranging from 0.493 to 0.729). However, none of the correlation coefficients exceeded 0.80, excluding the issue of multicollinearity [44]. The variables’ correlations reached statistical significance (p < 0.05).
Path analysis testing of the hypothesized model
The model index result showed that the chi-square was 268.42, the df was 111, the χ2/df value of the model was 2.42, and all parameters (SRMR = 0.048, RMSEA = 0.049, CFI = 0.947, IFI = 0.935) met the requirements of the standard fitting value. Therefore, we considered the modified model a good fit, as shown in Fig. 2.
Fig. 2
Final model of the structural relations identified. ***p < 0.001,**p < 0.01, *p < 0.05
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Table 3 demonstrates that path analysis testing the hypothesized model. The standardized path coefficient was not close to 1, and the parameter estimation SE value was more significant than 0, indicating that the structural model parameters were reasonable. The coverage rate (CR) critical value was greater than 2, and the p-value was significant at 0.001, indicating that the parameters of the structural model are significant.
Table 3
Testing results of the hypothesis model
Dependent variable | Independent variable | path coefficient | SE | CR | P | R2 |
|---|---|---|---|---|---|---|
SDLA | EE | 0.189 | 0.083 | 2.268 | 0.023 | 0.473 |
LM | 0.436 | 0.072 | 6.044 | < 0.001 | ||
EI | 0.215 | 0.072 | 2.999 | 0.003 | ||
MA | EE | 0.224 | 0.078 | 2.876 | 0.004 | 0.569 |
SDLA | 0.520 | 0.076 | 6.863 | < 0.001 |
Assessment of mediating paths
Table 4 shows the results of the mediation model tested using the bootstrapping method (Shrout, 2002). The results indicated the relationship of the educational environment with metacognitive awareness, indirect effect 0.099, 95% CI (0.002–0.211), direct effect 0.224, 95% CI (0.041–0.421), and total effects 0.323, 95% CI (0.113–0.545). The CI did not include 0, indicating that self-directed learning has a mediating impact, and the mediating contribution rate was 30.6% (0.099/0.323).
Table 4
Bootstrap metacognitive awareness analysis of total, direct, and indirect effects
Variables | Effect | Point estimate | Product of coefficients | Bias-Corrected 95%Cl | Percentile 95%Cl | |||||
|---|---|---|---|---|---|---|---|---|---|---|
SE | Z | Lower | Upper | Lower | Upper | |||||
EE→ MA | Total effects | 0.323 | 0.107 | 3.007 | 0.113 | 0.545 | 0.111 | 0.540 | ||
EE→ MA | Direct effect | 0.224 | 0.097 | 2.303 | 0.041 | 0.421 | 0.033 | 0.415 | ||
EE→SDLA→ MA | Indirect effect | 0.099 | 0.054 | 1.816 | 0.002 | 0.211 | 0.001 | 0.209 | ||
LM→SDLA→ MA | Indirect effect | 0.112 | 0.054 | 2.067 | 0.020 | 0.230 | 0.019 | 0.223 | ||
EI→SDLA →MA | Indirect effect | 0.227 | 0.073 | 3.125 | 0.102 | 0.395 | 0.096 | 0.384 | ||
The results regarding the relationship of learning motivation with metacognitive awareness had an indirect effect of 0.112, 95% CI (0.020–0.230), and those regarding the relationship of emotional intelligence with metacognitive awareness had an indirect effect of 0.227, 95% CI (0.102–0.395). The CI did not include 0, indicating that self-directed learning ability indirectly influences the associations of learning motivation with metacognitive awareness. And self-directed learning ability indirectly influences the associations of emotional intelligence with metacognitive awareness.
Discussion
The results indicate a significant improvement in metacognitive awareness with age and year level, while no significant difference was observed for sex, consistent with previous studies [45]. The enhancement in metacognitive awareness among students as they age and progress through their academic grades can be attributed to their accumulating experience in nursing practice and deepening comprehension of the discipline’s knowledge. This growth fosters a heightened recognition of their learning requirements, empowering them to devise more effective study plans, vigilantly monitor their educational progress, and adeptly assess their achievements [14]. In addition, nursing professional environment of college education, the accumulation of knowledge, and personal growth experiences contribute to the development of metacognitive awareness in nursing students [38].
The self-directed learning ability score of female nursing student was significantly higher than that of their male counterparts, a finding that contradicts previous reports [46, 47]. This discrepancy suggests that gender influences the variation in nursing students’ self-directed learning abilities. The underlying reason may be the substantial differences in learning methods and interests between male and female nursing majors [48]. Prior studies have shown that male students often need a comprehensive understanding of the professional values inherent in the nursing field. In many cultures, nursing is stereotypically viewed as a female profession. Such perceptions could deter males from pursuing or studying nursing due to fears of social prejudice and discrimination, leading to diminished motivation for self-directed learning [49]. Consequently, researchers must focus on enhancing the self-directed learning ability of male nursing students.
Correlation analyses confirmed the positive associations among educational environment, learning motivation, emotional intelligence, self-directed learning ability, and metacognitive awareness (p < 0.01), see Table 2. Previous research has shown that the educational environment is an external factor affecting the development of nursing students’ metacognitive awareness [50]. Emotional intelligence and learning motivation are the driving forces behind metacognitive awareness, helping to select of the most creative ideas [13, 51], and self-directed learning ability is the driving force for developing metacognitive awareness in nursing students [52]. Our findings again verified this point of view. Thus, focusing on the educational environment, learning motivation, emotional intelligence, and self-directed learning ability are vital factors for improving the metacognitive awareness of nursing students [47, 48].
In addition, the study also investigated the potential influence of self-directed learning abilities in explaining these associations. Table 3 reveals that nursing students with higher scores in educational environment, learning motivation, and emotional intelligence demonstrated enhanced self-directed learning ability. A more robust assessment of the educational environment correlates with an increased ability of students to utilize educational resources effectively. This result enables them to establish conditions for self-directed learning based on their individual learning needs, thereby enhancing their self-directed learning abilities [52]. Research suggests that students with heightened learning motivation often opt for proactive learning strategies and formulate self-directed learning plans, acting as intrinsic motivators for improving self-directed learning abilities [11]. Emotional intelligence is an emotional catalyst for self-directed learning, fostering the vitality of students’ self-directed learning abilities [53]. These findings affirm that the environment, personal motivation, and intelligence are pivotal factors that trigger students’ self-directed learning ability [54].
The results of path analysis confirm the direct links between self-directed learning ability and metacognitive awareness. Furthermore, self-directed learning ability serves as the intermediary mechanism between the educational environment and metacognitive awareness. Learning motivation and emotional intelligence could indirectly affect metacognitive awareness by influencing self-directed learning ability (see Table 4). These findings underscore the importance of paying greater attention to the direct and indirect effects of self-directed learning ability when seeking to enhance metacognitive awareness in nursing students. Self-directed learning ability represents a form of independent learning in which learners formulate learning plans and engage in self-guided learning activities. This capacity encompasses essential personal attributes, such as goal orientation, autonomy, and self-management [55]. Prior studies have indicated that as students’ self-directed learning ability strengthens, so does their proficiency in planning, monitoring, and evaluating the learning process [9, 25]. This proficiency is conducive to reflecting on, comprehending, and controlling their thought processes, enhancing metacognitive awareness. Consequently, developing a systematic and targeted strategy to improve self-directed learning ability emerges as a practical approach to elevating the metacognitive awareness of nursing students.
In summary, enhancing metacognitive awareness among nursing students can be achieved through three strategic approaches. First, provide a diversified education resource environment construction, including high-quality online course resources, excellent teachers’ academic classroom sharing, and Artificial Intelligence self-study room, encouraging students to pursue self-directed learning based on their interests and needs, thereby facilitating the accumulation of knowledge of cognition. Secondly, maintain the motivation of nursing students. Nursing case-oriented teaching is adopted to guide nursing students to use engineering and information technology to solve practical nursing problems, increase their information use and processing ability, stimulate students’ self-directed learning motivation, and improve regulation of cognition capabilities. Thirdly, it promotes the development of emotional intelligence in nursing students. By reforming immersive learning methods such as role-playing, situational simulation, and reflective learning, nursing students of different genders are encouraged to express their emotions, challenge themselves, experience professional values, harvest the results of self-directed learning, and improve metacognitive awareness.
Limitations
Several limitations of our study should be acknowledged. Firstly, the study collected data from nursing students at two public research universities in China. Hence, the generalization of the conclusions of this research to other cultural backgrounds and geographic areas may be limited, and these findings cannot represent the condition of all nursing students. Secondly, this study may involve social expectation bias. Although the questionnaire is voluntary and anonymous, it is completed using self-assessment methods, and nursing students may subconsciously comply with the expectations of teachers and society when completing the questionnaire. Therefore, nursing students may overestimate their abilities. Thirdly, this study is cross-sectional and thus needs to be interpreted cautiously. More longitudinal research evidence is required in the future to strengthen the results of this study.
Conclusion
This study revealed that metacognitive awareness in nursing students significantly increases with age and year level. Female nursing students demonstrated higher self-directed learning abilities than their male counterparts. Furthermore, the educational environment and self-directed learning ability directly influence metacognitive awareness, while learning motivation and emotional intelligence affect it indirectly through self-directed learning ability. These findings shed light on the internal mechanisms driving metacognitive awareness and highlight the crucial role of self-directed learning ability. Consequently, this research offers a theoretical foundation for designing educational interventions to enhancing metacognitive awareness among nursing students.
Acknowledgements
The author is very grateful for the support of domestic and foreign researchers and the cooperation of the research subjects who participated in this study.
Declarations
Ethics approval and consent to participate
The protocol was approved by the Medical Ethics Committee of Huzhou University (number 202012-JG02). The study followed the ethical principles of the Declaration of Helsinki, and each participant was informed of the aims and details of the research.Informed consent was obtained from all respondents.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
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