Self-regulation, cognitive flexibility, and resilience among students: A mediating analysis

doi:10.21203/rs.3.rs-3572514/v1

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Self-regulation, cognitive flexibility, and resilience among students: A mediating analysis

https://doi.org/10.21203/rs.3.rs-3572514/v1

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Background

This study aimed to investigate the mediating role of self-regulation in the relationship between cognitive flexibility and resilience among students.

Method

This was a cross sectional study on a random sample of students from Tehran and Karaj universities (two metropolitans in Iran) during 2022 and 2023 academic year. Data collection instruments included the Bouffard's Self-Regulation Scale, the Cognitive Flexibility Inventory (CFI), and the Connor-Davidson Resilience Scale (CD-RSC). The data were analyzed by performing the structural equation modeling using SPSS and AMOS software, to examine the relationships among variables.

Results

In all 302 students participated in the study. The mean age of students was 25.8 (SD = 4.05) years. The findings indicated that cognitive flexibility could account for 27% of the variance in self-regulation and 48% of the variance in resilience. Self-regulation, in turn, explained 21% of the variance in resilience. The goodness of fit indices validated the proposed model. Furthermore, structural equation analysis revealed the significance of the final model's direct path coefficients, underscoring the mediating role of self-regulation between cognitive flexibility and resilience among students.

Conclusion

The findings indicated that a pivotal interrelationship among self-regulation, cognitive flexibility, and resilience in students. The significant positive relationship among these constructs underscores the importance of fostering self-regulatory practices and cognitive flexibility in educational settings.

Self-Regulation

Cognitive flexibility

Resilience

Students

The transition into university marks a pivotal and challenging period in an individual's academic journey. As students embark on this new phase, they not only shoulder crucial roles and responsibilities toward future contributions to public health but also grapple with numerous pressures and changes [1]. These challenges have the potential to profoundly impact students' mental health. For instance, 60% of university students reported high stress levels during their academic years [2, 3]. It is thus evident that fostering resilience becomes paramount to ensure optimal academic performance and the overall mental well-being of students [4].

Therefore, it becomes evident that fostering resilience is integral to promoting optimal academic performance and maintaining the quality of students' mental health [5]. Resilience can be characterized as an individual's capacity to adapt constructively to stressful and challenging circumstances. Resilience emerges when individuals confront threatening and challenging situations head-on, rather than evading them. Furthermore, a significant correlation exists between resilience and overall life satisfaction among students [6]. Consequently, students who possess a high degree of academic resilience demonstrate greater tolerance in stressful situations, exhibit enhanced cognitive flexibility when faced with stressors, and despite challenging circumstances, persistently strive to attain their objectives [7].

In the study conducted by Burton et al, cognitive flexibility was identified as one of the five critical and influential factors contributing to resilience. Individuals with higher resilience tend to perceive negative situations more realistically and flexibly compared to those with relatively lower resilience [8, 9]. students possessing suitable cognitive flexibility have the capacity to appraise various situations from multiple viewpoints. They can deeply analyze scenarios, assess different alternatives, and select fitting strategies to navigate unfamiliar challenges and circumstances [10]. A significant individual determinant of student success or failure is self-efficacy, which is greatly influenced by various factors, among which self-regulation strategies stand out as particularly crucial [11]. Self-regulation, a foundational facet of human performance, plays a pivotal role in the pursuit and attainment of personal goals. Self-regulation constitutes a behavioral, cognitive, emotional, and physiological framework encompassing individuals' conscious or unconscious efforts to regulate states or responses. Ashmita and Analakshmi's study on relationship between self-regulation and attachment style with resilience and academic progress among att-risk rural adolescents revealed interesting findings. The results demonstrated that self-regulation was the sole predictor of resilience. Moreover, it was found that self-regulation positively predicted academic achievement, whereas attachment avoidance negatively impacted it [12]. In a different study conducted by Korhan et al. (2021), titled "Examination Anxiety of Young People Preparing for University Entrance Exams during the COVID-19 Outbreak: The Role of Cognitive Flexibility and Self-regulation," it was demonstrated that individuals with high cognitive flexibility and effective self-regulation experienced lower levels of test anxiety compared to those with low cognitive flexibility and ineffective self-regulation. Thus, both cognitive flexibility and self-regulation play a substantial role in moderating examination anxiety among young individuals [13].

Furthermore, research conducted by Waziri, Ghanbari, and Tejali, revealed that cognitive flexibility had a substantial direct effect on psychological toughness, and psychological toughness in turn impacted self-regulation.

The studies conducted illustrated that resilience, cognitive flexibility, and self-regulation generally exhibited a positive and significant correlation with regard to managing and maintaining psychological stability. However, given the scarcity of research specifically addressing the role of self-regulation, cognitive flexibility, and resilience in student populations, and considering that most studies have concentrated on the implications of these constructs in other fields and diverse societies, this study endeavored to explore the mediating role of self-regulation in the relationship between cognitive flexibility and resilience in students.

Given the context of existing research on the relationship between self-regulation, cognitive flexibility, and resilience, this study sought to concentrate specifically on these dimensions to assess what role does self-regulation play in students' cognitive flexibility and resilience?

Design and participants

This was a cross-sectional study carried out on a sample of university students assessing the relationship between self-regulation, cognitive flexibility, and resilience.

Sample size and sampling

The statistical population of the study included all university students from two metropolitans (Tehran, Karaj), Iran during the academic year 2022–2023. The inclusion criteria consisted of the following conditions: 1) signing a written informed consent form, 2) being a student in the current semester of 2022–2023, and 3) be aged 18 to 35 years. The exclusion criteria included dislike in participating in the study.

Procedure and data collection

Participants were initially briefed about the study's objectives. They provided their affirmation by signing an informed consent form. They then proceeded to complete a series of self-reported questionnaires, which were designed and administered online. These included the Buford Academic Self-Regulation Scale, the Connor-Davidson Resilience Scale (CD-RISC), and the Cognitive Flexibility Inventory (CFI). While these tools were originally developed and standardized outside Iran, they have been subsequently adapted and validated within an Iranian context using confirmatory factor analysis. The details of this validation are discussed in subsequent sections. Participation was voluntary, and participants retained the option to withdraw from the study at any point. The entire study, including data collection, adhered to the ethical standards established by our research committee. No financial incentives were offered to the participants for their involvement.

Measures

In addition to a demographic questionnaire collecting information on participants’ age, gender, and education, the following questionnaires were administered.

Connor–Davidson Resilience Scale (CD-RISC)

The CD-RISC is a 25-item questionnaire that assesses the individual’s ability to cope with stress and adversity. Items are rated on a 5-point Likert scale ranging from 0 (‘‘not true at all’’) to 4 (‘‘true nearly all the time’’). Preliminary research on the CD-RISC’s psychometric properties in the general population and clinical samples revealed sufficient internal consistency, convergent and divergent validity, and test-retest reliability [14]. According to exploratory factor analysis, the CD-RISC might be multidimensional, with factors matching personal competence/tenacity, positive acceptance of change/secure relationships, trust in one’s instincts/tolerance of negative affect, spirituality, and control. We used CD-RISC total scores in the present study. In Iranian populations, validity coefficient of the scale has been reported between 0.41 and 0.64. Also, the reliability was acquired to be 0.89 using Cronbach’s alpha [15]. In this research, the coefficient alpha obtained was 0.91 for the total score of CD-RISC.

Cognitive Flexibility Inventory (CFI): The CFI is a 20-item self-report questionnaire developed for aspects of cognitive flexibility that enable people to challenge and replace maladaptive thoughts with more adaptive ones. Items are rated on a 7-point Likert-type scale to define the respondent’s approach to challenging situations accurately. We used CFI to assess three features of cognitive flexibility: 1) the person’s tendency to identify problematic situations as controllable; 2) the ability to comprehend multiple alternative explanations for life events and human behaviors; and 3) the ability to produce multiple alternative solutions to difficult situations(Dennis, Vander Wall., 2010). Dennis and Vander Wall reported that CFI had good to excellent internal consistency, and also test-retest reliability was high for the total CFI score and its subscales. The Iranian version of the CFI has desirable levels of reliability and validity. In this version, the result of factor analysis indicated three factors revealed 56.02% of the variance: Control, Alternatives, and Alternatives for Human Behaviors. The test-retest and Cronbach’s alpha coefficients for the CFI reliability were 0.71 and 0.90, respectively [16]. In this study, the coefficient alpha was calculated to be 0.90 for the total score of CFI.

Buford’s Self-Regulation Questionnaire: The 14-item self-regulation questionnaire was developed by Buford et al, and standardized by Kadivar in 2001. The total validity coefficient of the questionnaire based on Cronbach’s alpha was calculated to be 0.71. The validities of the sub-scales of cognitive and metacognitive strategies were 0.70 and 0.68, respectively. Regarding the structure, the factor results showed that the correlation coefficient of the questions was acceptable, and the evaluation tool consisted of two factors. The value of the factors was acceptable, and the tool could determine 0.52 of the self-report variance. The structural validity was satisfactory. There were five possible answers for each question: “I totally agree,” “I agree,” “I’m not sure,” “I disagree,” and “I totally disagree.” Each question was scored from 1 to 5, except for questions 5, 13, and 14, which were scored in the reverse [17, 18].

Statistical analyses

The data obtained from the questionnaires were analyzed using Pearson’s correlation coefficient method and structural equation modeling (SEM) in Lisrel 8.7 and SPSS-27 software. The collected data underwent analytical analysis in two distinct parts. The initial part involved an examination of the sample's descriptive and demographic characteristics, alongside the calculation of central and dispersion indicators, including frequency, percentage, mean, standard deviation, and standard error, for the criteria data. The subsequent part of the analysis encompassed the utilization of inferential statistics to analyze the questionnaire data. This encompassed employing the structural equation method, specifically path analysis, and linear regression. The path analysis process served to assess the degree of alignment between the theoretical-causal model and the empirical data.

Characteristics of the study sample

A total of 302 students participated in the study (146 men and 156 women). The mean age of students was 25.8 (SD = 4.05) years ranging from 18–35. Of these, 32.1% of the participants were undergraduate, the remaing students were postgraduate students (55.3% master, and 12.6% Ph.D students). The characteristics of students are shown in Table 1.

Table 1

Distribution of Samples (n = 302)
Gender	Frequency	Percentage
Female	156	51.7
Male	146	48.3
Educational level
Bachelor	97	32.1
Masters	167	55.3
Doctorate	38	12.6
Age group
18 to 20 years	28	9.27
21 to 25 years	125	41.39
26 to 30 years	107	35.43
31 to 35 years	42	13.91

Distribution of research variables

To ascertain the nature of data distribution, the Kolmogorov-Smirnov test, skewness, and kurtosis techniques were implemented. Prior to the validation of the hypotheses, it was essential to verify the normality of the data distribution. This examination aids in deciding whether parametric or non-parametric statistical tests would be most suitable for hypothesis testing. The outcomes of these assessments can be found in Table 2. The findings outlined that the p-value resulting from the Kolmogorov-Smirnov test for the aforementioned variables exceeded the threshold of 0.05. This suggests that the distributions of resilience, flexibility, self-regulation, and their respective components did not significantly deviate from a normal distribution. As per widely accepted statistical standards, a distribution may be presumed normal if the skewness falls within the range of -2 to + 2 and kurtosis lies within − 3 to + 3. Given that none of the data distributions for the research variables transgressed these bounds, it can thus be inferred that the data distribution aligns closely with normality.

Table 2

Distribution of research variables
	Kolmogorov–Smirnov test	Significance	Skewness	Standard deviation	Mean score	Possible total score
Self-regulation
Cognitive self-regulation	0.112	0.065	-0.73	4.60	23.56	34
Metacognitive self-regulation	0.106	0.078	-1.42	6.57	24.12	34
Cognitive-Flexibility
Replacing ineffective thoughts with effective ones	0.117	0.061	-0.61	16.34	58.84	83
Control	0.101	0.085	-0.65	9.67	35.90	56
Resilience
Perception of individual competence	0.115	0.064	-1.05	8.04	19.11	32
Trust in individual instincts and tolerate negative emotions	0.112	0.068	-0.54	4.46	15.75	28
Positive acceptance of change and secure relationships	0.121	0.056	-1.11	5.15	11.87	20
Control	0.12	0.058	-1.03	2.93	7.02	12
Spiritual influences	0.127	0.054	-0.27	2.46	4.62	8

Examining Pearson product-moment correlation

To examine the interrelationship among the components of resilience, flexibility, and self-regulation in students, Pearson's moment correlation coefficient was employed. The outcomes of this examination are detailed in Table 3. The data gleaned from Pearson's correlation test, as presented in Table 3, reveal a significant positive correlation between the components of resilience, flexibility, and self-regulation within the student population. Given a meaningful relationship among variables, the mediating role of self-regulation and the relationship between flexibility and resillence were further explored.

Table 3

Pearson correlation among constructs of the study questionnaires
	9	8	7	6	5	4	3	2	1
Resilience
9. Spiritual influences	1	-	-	-	-	-	-	-	-
8. Control	.555**	1	-	-	-	-	-	-	-
7. Positive acceptance of change and secure relationships	.695**	.811**	1	-	-	-	-	-	-
6. Trusting individual instincts and tolerating negative emotions	.555**	.825**	.689**	1	-	-	-	-	-
5. Perception of individual competence	.647**	.872**	.877**	.816**	1	-	-	-	-
Cognitive-Flexibility
4. Control	.116*	.262**	.254**	.262**	.258**	1	-	-	-
3. Replacing ineffective thoughts with effective ones	.143*	.222**	.236**	.231**	.233**	.603**	1	-	-
Self-regulation
2. Metacognitive self-regulation	.589**	.774**	.762**	.735**	.814**	.169**	.236**	1	-
1. Cognitive self-regulation	.641**	.653**	.767**	.574**	.758**	.206**	.263**	.809**	1

Summary of model findings

Utilizing Structural Equation Modeling (SEM), as illustrated in Fig. 1, we precisely examined the relationships between flexibility, self-regulation, and resilience. Before analyzing, we confirmed several foundational assumptions. The chosen sample size of 302 participants aptly surpassed the recommended threshold for SEM. Earlier stages had already validated the normality of data distribution and variable correlation. Our Kaiser-Meyer-Olkin (KMO) Index was a solid 0.895, and Bartlett's Test of Sphericity affirmed the suitability of our correlation matrix with a score of χ^2 = 3832.028 (p < 0.001).

According to the findings, flexibility displayed a robust positive influence on both resilience (β = 0.48, t = 5.75, p < 0.001) and self-regulation (β = 0.56, t = 6.86, p < 0.001). According to the Table 5 flexibility can explain 27% of the variance of self-regulation and the ability to explain 48% of the variance of resilience. Self-regulation also explains 42% of the variance of resilience. Similarly, self-regulation had a marked positive direct effect on resilience (β = 0.52, t = 6.29, p < 0.05). Assessing the model's fit using established indices, such as the chi-square to degrees of freedom ratio and the Comparative Fit Index (CFI), yielded results in line with accepted standards, verifying the model's appropriateness. Detailed coefficients of the model's pathways can be found in Table 4. Overall, these results compellingly highlight self-regulation's mediating role in the dynamic between flexibility and resilience among students.

Table 5

Total, direct and indirect standard coefficients in the model
Paths	Total effect	Direct effect	Indirect effect	Variance explained
Flexibility over self-regulation	0.56	0.56	-	0.27
Flexibility over resilience	0.77	0.48	0.52×0.56 = 0.29	0.48
Self-regulation on resilience	0.61	0.61	-	0.42

The study investigated the objective of this study was to explore the mediating role of self-regulation in the relationship between cognitive flexibility and resilience in students. Our findings corroborated our initial hypothesis, demonstrating that self-regulation indeed serves as a mediator between cognitive flexibility and resilience. A strong and substantial positive direct effect was observed between flexibility, resilience, and self-regulation. Moreover, the direct impact of self-regulation on resilience in students was also noted to be significantly positive. The post-analysis results further indicated that cognitive flexibility accounted for 27% of self-regulation variance, while also explaining a significant 48% variance of resilience. Similarly, self-regulation contributed to 21% of resilience variance. This investigation, grounded on a series of theoretical premises and empirical evidence, put forth and scrutinized a multivariate model delineating the interplay between self-regulation, cognitive flexibility, and resilience. The inferential section of the findings highlighted that self-regulation played a significant mediating role, facilitating cognitive flexibility and fostering resilience among students, as determined by the collected data. Hence conceptual model displayed a suitable fit thereby substantiating the research hypothesis that self-regulation acts as a bridge linking cognitive flexibility and resilience in students.

The results from the study further reinforce the findings from previous research, providing additional support for the crucial role of self-regulation in fostering cognitive flexibility and resilience in students. These results align well with prior studies that have elucidated the interconnected nature of these constructs. A rich body of evidence already underlines the significant positive associations among cognitive flexibility, self-regulation, and resilience, This attests to the replicability of the phenomena observed and the robustness of these constructs' relations.

The study has substantiated the mediating role of self-regulation, a concept that previous research has suggested but has been less conclusive. Our study thus fills a crucial gap in the literature by statistically confirming this mediator role, which will undoubtedly enrich the existing knowledge base and provide a platform for future research in this area.

The role of self-regulation in influencing cognitive flexibility among students can be elucidated as follows: students exhibiting a higher degree of self-regulation tend to demonstrate enhanced cognitive flexibility. This correlation indicates that individuals proficient in managing their thoughts, emotions, and behaviors, when confronted with academic or life challenges, are likely to exhibit adaptable thinking and flexible problem-solving strategies [19].

Such individuals are often better prepared to navigate their academic responsibilities, engage in meaningful social interactions, tackle complex problems, and deal effectively with the multifaceted demands of collegiate life. Further, those who display high cognitive flexibility are typically adept at setting realistic goals and formulating strategic plans to realize them. They possess the capacity to shift their cognitive strategies, adopt diverse approaches, and entertain various perspectives - crucial facets of self-regulation [20].

This bidirectional relationship illustrates that bolstering one of these characteristics can trigger the enhancement of the other. For instance, individuals with robust self-regulation skills may demonstrate greater adaptability and flexibility when faced with adversity, allowing them to engage with and navigate these challenges with greater ease. Conversely, individuals who exhibit elevated cognitive flexibility typically demonstrate superior capacity in formulating appropriate goals and devising effective strategies to attain them, as suggested by Lawrence (2015). This is indicative of the interdependence and mutually reinforcing relationship between cognitive flexibility and self-regulation.

Limitations

While our findings offer promise, they should be interpreted in light of certain limitations. First, our sample size was relatively small, which may compromise the generalizability of our results to broader populations. Second, as with many types of research, it was challenging to control for all potential intervening or disruptive variables that could influence our outcomes. Lastly, our study predominantly focused on participants within the age range of 18–35. This specificity limits the direct applicability of our findings to other age groups.

Future directions

we recommend that future research on this topic should incorporate larger and more diverse samples to ensure broader applicability of the findings. Moreover, the exploration of this model across different educational levels within the student population could further enrich our understanding of these relationships.

In practical terms, these findings carry significant implications for the educational sector. As such, it would be beneficial to investigate the efficacy of cognitive treatments and exercises geared toward bolstering self-regulation, with the aim of enhancing cognitive flexibility, resilience, and, ultimately, academic performance and mental well-being in students.

In conclusion, the study revealed the central role of self-regulation in mediating the relationship between cognitive flexibility and resilience among students. Our findings not only resonate with previous research but also fill an existing gap by quantifying this relationship. The intertwined nature of these traits suggests that strengthening one could potentially enhance the others, emphasizing the need for an integrated educational approach. Given the implications for fostering adaptability and success in students, educators and policymakers should prioritize initiatives that emphasize these critical skills. This study sets a foundation for both future research and the development of targeted educational strategies.

Ethics approval and consent to participate

The study was approved by the Research Ethics Committee of Islamic Azad University- Science and Research Branch(IR.IAU.SRB.REC.1402.080). All methods were performed in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants before they participated in the study.

Consent for publication

Not applicable

Availability of data and materials

The data is available from the first investigator (MNK) on reasonable request.

Competing interests

The authors declare that they have no competing interests

Funding

None.

Authors' contributions

MNK was the main investigator, collected the data, and wrote the initial draft. MB supervised the study and contributed to all aspects of the study. MZ was responsible for data analysis and writing process. DF and AM critically reviewed the manuscript and provided the final draft. All authors read and approved the final manuscript.

Acknowledgements

We extend our gratitude to the competent authorities of the Islamic Azad University of E-Campus for their support during this research. We also thank all participants in the current study.

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No competing interests reported.

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You are reading this latest preprint version

Self-regulation, cognitive flexibility, and resilience among students: A mediating analysis

Status:

Version 1

Abstract

Background

Method

Results

Conclusion

Figures

Introduction

Methods

Design and participants

Sample size and sampling

Procedure and data collection

Measures

Statistical analyses

Results

Characteristics of the study sample

Distribution of research variables

Examining Pearson product-moment correlation

Summary of model findings

Discussion

Limitations

Future directions

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1