| Home | E-Submission | Sitemap | Contact Us |  
top_img
J Korean Acad Fundam Nurs > Volume 31(2); 2024 > Article
Jo and Hwang: Mediating Effect of Academic Emotion Regulation on the Relationship Between Self-Determined Learning Motivation and Learning Flow in Nursing Students in Remote Online Classes

Abstract

Purpose

This descriptive research study explores the relationship between remote online classes, nursing students’ self-determined learning motivation and learning flow, to identify the mediating effect of academic emotion regulation.

Methods

The study sample comprised 147 third- and fourth-year nursing students from two universities in Jeonbuk State, all of whom had clinical practice experience. Descriptive statistics, independent t-tests, and one-way analysis of variance were conducted using SPSS/WIN 23.0 for data analysis. Hayes’ PROCESS macro Version 3.5 was used to verify the significance of the indirect effects of the variables.

Results

Self-determined learning motivation had a significant positive effect on the mediating variable, academic emotion regulation(β=.21, p=.001), and on learning flow. Additionally, academic emotion regulation significantly positively influenced learning flow. When considering the mediating effect of academic emotion regulation, the indirect effect size between self-determined learning motivation and learning flow was .07, with a bootstrapping confidence interval ranging from 0.02 to 0.13, excluding zero, which was statistically significant, indicating a mediating effect.

Conclusion

The findings confirm that academic emotion regulation is a crucial variable that can enhance students’ learning flow, thereby maximizing educational effectiveness during rapid changes in online education methods. Consequently, educational programs should be developed that consider the emotional aspects of learning to improve academic emotion regulation. Furthermore, universities should establish educational policies and measures that expand various digital education platforms to reflect and respond to the online education environment.

INTRODUCTION

As the educational environment is rapidly evolving due to the Fourth Industrial Revolution and COVID-19, online education, which formerly was mainly practiced in speci-alized schools such as cyber universities [1,2], is now being employed by many universities through a mix of in-person and non-face-to-face classes. This transition has notably af-fected nursing education, where the necessity for both the-oretical and practical learning is paramount; in particular, online courses have led to reduced learning outcomes due to less direct interaction and engagement between teachers and students [3-5]. In non-face-to-face online education in learner-centered environments, learning flow - perceiving learning as a meaningful challenge, believing in one's abilities, and being fully immersed and willing to engage in difficult tasks-is essential for learning outcomes [6,7]. Learning flow is critical because it increases motivation and the likelihood of success [8]. Similarly, learning motivation is essential for persistence in studies and influences performance-related factors, including initiation, continuation, goal setting, and outcomes [9]. In nursing education, self-motivation and autonomous learning are crucial, and they involve choosing one's learning path and self-directing behavior [10]. Self-determination and intrinsic motivation are linked to greater persistence and academic achievement [11]. Therefore, examining the relationship between learning flow and self-determined learning motivation is key to advancing academic achievement and nursing competency. In online learning, academic emotion regulation is vital as it helps learners manage their emotions, which vary in their effects on learning outcomes [12]. Positive emotions can enhance voluntary effort, while un-pleasant emotions may lead to motivational loss [13]. Effective emotion regulation is associated with better cognitive processing, task performance, and motivation control, pre-dicting academic success [12,14,15]. Therefore, learners’ emotional regulation is critical to the academic process [14]. Academic emotion regulation is closely related to high-level cognitive processing, task performance, and motivation control. It has a direct and indirect influence on academic achievement and is a predictor of successful academic performance [12]. Proficient students exhibit strong problem-solving abilities and accomplish their goals [15]. Therefore, challenges emerge in achieving learning outcomes due to reduced direct contact with instructors and lack of interaction owing to online classes [3,4]. In online classes, we aim to identify the mediating effect of academic emotion regulation to promote learners’ active participation and cognitive function so they can become more self-determined in class and experience learning flow in relation to motivation. Immersion is essential for online real-time classes to lead to learning outcomes. Learning immersion is a pleasant state in which one is completely in-volved in educational activities and feels challenged and accomplished in meeting difficult tasks, thereby improving outcomes [8]. Research shows that self-determined learning motivation directly influences learning flow and positively affects learning-related variables [11,16,17]. Academic emotion regulation also, directly and indirectly, impacts learning flow [18], with self-determined learning motivation mediating this relationship in the perceived teacher-stu-dent dynamic [19]. As the paradigm of nursing education is becoming performance-oriented and rapidly shifting to an online education environment, learning engagement has been studied as a critical variable. For example, learning engagement is examined as a mediator in the effects of online real-time classes on performance-oriented learning outcomes [20] and those of self-friendship among nursing students who take online classes [21]. However, the mediating effect of academic emotion regulation in self-de-termined learning motivation on engagement in nursing students, which is necessary for enhancing their learning capabilities and performance-based education, has not been studied. This study seeks to enhance nursing students’ learning capabilities and performance-based outcomes in online education by exploring the impact of self-determined learning motivation on learning flow and the mediating role of academic emotion regulation. The goal is to provide data to inform the development of teaching methods and educational strategies that foster self-de-termined learning motivation, emotion regulation skills, and improved learning outcomes. This study aims to identify nursing students’ self-determined learning motivation and flow. Furthermore, it examines the mediating effect of academic emotion regulation on the relationship between self-determined learning motivation and flow.

METHODS

1. Study Design

This study used descriptive research to examine the mediating effect of academic emotion regulation on self-determined learning motivation and learning flow among nursing students.

2. Research Participants

We recruited third- and fourth-year nursing students aged 19 to 30 from nursing departments offering clinical practice at two universities in J province. Eligible participants had taken online classes in the first semester of the academic year and obtained approval from their university heads to join the study. Recruitment occurred via department bulletin board announcements from December 1 to 21, 2021. The classes were conducted face-to-face, online, and via video lectures. They lasted 14 weeks in 2-hour sessions. Using G*Power 3.1 for sample size calcu-lation, a medium effect size of .15, an ⍺ of .05, and a power of .90, and 9 predictors were assumed for multiple regression analysis based on prior research [21]. This analysis determined a sample size of 141 participants, considering self-determined learning motivation and academic emotional regulation as independent variables. To account for a potential 10% attrition rate, we distributed 155 questionnaires, ultimately analyzing 147 completed responses after discarding eight for being incomplete.

3. Measurements

Self-determined learning motivation was assessed using the Self-Regulation Questionnaire-Academic (SRQ-A), originally developed by Ryan and Connell [15] and adapt-ed by Park et al. [21] for Korean university students. Sub-sequent modifications by Cha and Eom [22] tailored the SRQ-A to evaluate college student motivation, resulting in a 12-item instrument with six items each for confirmed ac-commodation and intrinsic adjustment. Total scores ranged from 12 to 60. Cha and Eom [22] reported excellent reliability (⍺=.90), which was mirrored in this study (⍺=.91). Academic emotion regulation was gauged using Yu and Lee's [10] 24-item tool with four sub-factors, each comprising six items: emotion awareness, goal-consistent behav-iors, positive reevaluation, and approach to strategy. The total scores ranged from 24 to 120. Its reliability was excellent in both Yu and Lee's (⍺=.93) and the current (⍺=.89) study. The Flow State Scale (FSS), created by Jackson and Marsh [24] and refined by Kim [25], measures learning flow in adult education with 14 items on a five-point Likert scale; higher scores indicate greater flow. The total score range was 14 to 70. Initially, the FSS showed good reliability (⍺=.83), which improved in Kim's study (⍺=.93) and remained high in this research (⍺=.88). Participant demographics-gender, age, grade, religion, economic sta-tus, department satisfaction, and average grade-were col-lected via a questionnaire based on prior research [13,20,25]. Data were gathered from December 1 to 21, 2021, using structured questionnaires, with the study's purpose explained beforehand. Participants self-reported, taking about 15 minutes. Completed questionnaires were sealed in envelopes by the researcher to maintain participant anonymity.

4. Ethical Considerations

This study was conducted according to The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. This study was conducted after approval from the Korea National Institute for Bioethics Policy (No: P01-202105-21-015). Participants were students enrolled in nursing programs at four universities not affiliated with the researchers. For voluntary participation, recruitment notices were posted on the notice boards of the nursing faculties of each university to recruit students interested in the study. After the researcher explained the study's purpose and method, a consent form was presented for the participants to sign. The collected data would not be used for any purpose other than this research. Moreover, study participation could be withdrawn at any time, even after consent was given, with no disadvantage being incurred. It was explained that the research data were coded to ensure that confirming the participants’ identities would be impossible. In ad-dition, their personal information was securely stored to prevent data breaches.

5. Data Analysis

Data from this study were analyzed using SPSS/WIN Version 23.0. Participant characteristics, self-determined learning motivation, academic emotion regulation, and learning commitment were examined by calculating standard error, percentage, mean, and standard deviation. Differences in self-determined learning motivation, academic emotion regulation, and learning commitment, based on participant characteristics, were assessed using independent t-tests and one-way ANOVA, with Scheffé's test for post-hoc analysis. Pearson's coefficients determined the correlations among self-determined learning motivation, academic emotion regulation, and learning flow. The mediating effects of self-determined learning motivation, academic emotion regulation, and learning flow were tested using Hayes’ PROCESS macro 3.5, model 4, with the indirect effects’ significance evaluated through bootstrapping with a bias-corrected 95% CI and 10,000 resamples.

RESULTS

1. Differences in Learning Flow Based on the Participants’ General Characteristics

The impact of general characteristics on learning flow was examined, revealing that religion (t=2.91, p=.004), satisfaction with the nursing major (F=43.21, p<.001), and GPA (F=18.74, p<.001) significantly influenced learning flow. Post-hoc analysis indicated that students satisfied with their department and those with a GPA above 4.0 reported higher learning flow than their less satisfied and lower GPA counterparts (Table 1).
Table 1.
Difference in Learning Flow of the Participants According to General Characteristics (N=147)
Characteristics Categories n (%) Learning flow
M± SD t or F (p)
Gender Men 16 (10.9) 47.00±5.15 0.37 (.708)
Women 131 (89.1) 46.32±7.01
Age (year) 19∼25 105 (71.4) 46.49±7.41 0.26 (.799)
26∼30 42 (28.6) 46.17±5.13
Year in program 3rd 94 (63.9) 46.33±7.46 -0.15 (.879)
4th 53 (63.1) 46.51±5.58
Religion Yes 68 (46.3) 48.12±7.59 2.91 (.004)
No 79 (53.7) 44.91±5.73
Economic condition Good 16 (10.9) 46.00±6.53 1.82 (.166)
Average 108 (73.5) 45.93±6.16
Bad 23 (15.6) 48.87±9.36
Satisfaction with nursing major Satisfied a 64 (43.5) 51.15±6.04 43.21 (<.001)
Moderate b 72 (49.0) 43.22±5.15 b, c< a
Dissatisfied c 11 (7.5) 40.09±2.55
Grade point average ≤4.0 a 29 (19.7) 51.59±5.56 18.74 (<.001)
3.0∼4.0 b 81 (55.1) 46.41±6.77 c< b< a
≥3.0 c 37 (52.2) 42.30±4.86

M=mean; SD=standard deviation.

2. Participants’ Self-Determined Learning Motivation, Academic Emotion Regulation, and Degree of Learning Flow

Participants’ average self-determined learning motivation was 34.94±7.7, academic emotion regulation score was 79.59±10.8, and learning flow was 46.40±6.82 (Table 2).
Table 2.
Level of Self-Determination Learning Motivation, Academic Emotional Regulation, and Learning Flow Correlation among the Variables (N=147)
Variables M± SD (Sum±SD) Range Self-determination learning motivation Academic emotional regulation Learning flow
r (p) r (p) r (p)
Self-determination learning motivation 2.91±0.64(34.94±7.7) 1∼5(12∼60) 1
Academic emotional regulation 3.45±0.48(79.59±10.8) 1∼5(24∼120) .41 (<.001) 1
Learning flow 3.31±0.49(46.40±6.82) 1∼5(14∼70) .64 (<.001) .58 (<.001) 1

M=mean; SD=standard deviation.

3. Correlation between Participants’ Self-Determined Learning Motivation, Academic Emotion Regulation, and Learning Flow

Correlation analysis showed a significant positive relationship between self-determined learning motivation and both academic emotion regulation (r=.41, p <.001) and learning flow (r=.64, p<.001). Academic emotion regulation also correlated significantly with learning flow (r= .58, p<.001) (Table 2).

4. Mediating Effect of Academic Emotion Regulation on the Relationship between Self-Deter-mined Learning Motivation and Learning Flow

To examine the mediating effect of academic emotion regulation on the relationship between a subject's self-de-termined learning motivation and learning flow, we first confirmed the basic assumptions necessary for performing regression analysis. This confirmation was achieved using the Durbin-Watson index, which measures autocorrelation. The index score for the dependent variable (learning flow) ranged from 1.923 to 2.013, with values close to 2, indicating that the dependent variable was independent and free from autocorrelation. Furthermore, the results of the multicollinearity test showed that all tol-erance limits were 1.0 or less, ranging from .352 to .922, and the variance inflation factor ranged from 1.084 to 2.838, not exceeding the standard of 10. This result confirmed that no multicollinearity issues existed. Additionally, the p-p plot demonstrated a normal error term distribution, as the points were closely aligned with the 45° straight line. The standardized residual plot, used to confirm the homoscedasticity of the residuals, showed no discernible pattern, trend, or cycle centered on the average point of zero. However, the p-p plot displayed an irregular distribution.
To examine the mediating role of academic emotion regulation in the relationship between participants’ self-determined learning motivation and learning flow, we analyzed the mediation model using SPSS/PROCESS Macro 3.5. Before this analysis, variables such as religion, department satisfaction, and average grade, which demonstrated significant differences in learning flow, were converted into dummy variables and treated as control factors. The analysis revealed that self-determined learning motivation significantly positively influenced the mediating variable, academic emotion regulation (β=.21, p=.001), and the model's explanatory power was 17%. Consequently, self-determined learning motivation had a significant positive impact on learning flow, and academic emotion regulation also significantly enhanced learning flow. The model's explanatory power was 65% (Table 3, Figure 1).
Figure 1.
Mediating effects of academic emotional regulation in the relationship between academic emotional regulation and learning flow.
jkafn-31-2-225f1.jpg
Table 3.
Mediating Effects of Academic Emotional Regulation in the Relationship between Academic Emotional Regulation and Learning Flow (N=147)
Direct effect β SE t (p) 95% CI
LLCI ULCI
Self-determination learning motivation → Academic emotional regulation .21 .06 3.31 (.001) 0.09 0.34
R=.42, R2=.17, F=30.09, p<.001
Self-determination learning motivation → Learning flow .29 .05 6.38 (<.001) 0.20 0.38
Academic emotional regulation → Learning flow .31 .06 5.28 (<.001) 0.19 0.43
R=.81, R2=.65, F=36.76, p<.001

Dummy: Religion (yes), Satisfaction with nursing major (satisfied), Grade point average (4.0≤); CI=confidence interval; LLCI=lower level confidence interval; SE=standard error; ULCI=upper level confidence interval; β=standardized estimates.

To verify the significance of the total, direct, and indirect effects, we generated 10,000 samples using the bootstrapping method of the PROCESS macro. We confirmed the mediating effect of academic emotion regulation at 95% CI. First, the total effect size of the direct effect of self-determined learning motivation on learning flow and the indirect effect through the mediating variable, academic emotion regulation, was 0.36. The bootstrapping CI ranged from 0.26 to 0.45. The results were statistically significant as the CI did not include zero. Second, the magnitude of the direct effect of self-determined learning motivation on learning flow was 0.29, with the bootstrapping CI ranging from 0.20 to 0.38. Given that this range did not include zero, this result was also statistically significant. Third, the magnitude of the indirect effect, mediated by academic emotion regulation between self-determined learning motivation and learning flow, was 0.07. The bootstrapping CI ranged from 0.02 to 0.13, and because it did not contain zero, it was statistically significant, indicating a mediating effect (Table 4, Figure 1).
Table 4.
Verification of Total, Direct, and Individual Indirect Effects through Bootstrapping (N=147)
Variables Effect SE or Boot SE 95%CI
Boot LLCI Boot ULCI
Total effect 0.36 0.48 0.26 0.45
Direct effect 0.29 0.46 0.20 0.38
Self-determination learning motivation → Learning flow
Indirect effect 0.07 0.03 0.02 0.13
Self-determination learning motivation → Academic emotional regulation → Learning flow

CI=confidence interval; LLCI=lower level confidence interval; SE=standard error; ULCI=upper level confidence interval.

DISCUSSION

This study examined the degree of self-determined learning motivation and flow and academic emotion regulation in nursing students related to online teaching methods. It also investigated the mediating effect of academic emotion regulation on self-determined learning motivation and learning flow. The goal was to propose an educational plan to enhance students’ learning flow. In this study, the score for self-determined learning motivation was 2.91, which aligns with Lee and Han's research [27]. Their focus was on the academic adjustment of in-dividual nursing students, yielding an average score of 3.07. To enhance nursing students’ self-determined learning motivation, instructors must increase learners’ autonomy. This adjustment requires attitudes and stances that respect learners’ opinions and ideas concerning the learning environment and teaching methods. They should support learners’ autonomy as much as possible [24]. Therefore, based on continuous interaction with students, professors should enable students to be supporters and advi-sors for academic, career, and other issues, such as adjusting to the course and developing and applying comparative programs promoting self-control and autonomy. The academic emotion regulation score was 3.45 (79.59), com-pared to 3.37 in Noh and Kim's [28] study targeting nursing students and 80.64 in Tak and Cho's [29] research. There was no significant difference in academic emotion between Tak and Cho's [29] study and ours. We discovered that academic emotion regulation influences adaptation to college life [29]. Learners who concentrate on self-development, academic improvement, and understanding are more self-aware and better equipped to regulate their emotions [29]. Therefore, academic emotion regulation is a variable that enhances learning. Controlling academic emotions is necessary for nursing students who integrate theory and clinical practice. For this reason, various academic emotion regulation programs should be developed and included as part of courses and curricula. Nursing students, distinct from their counterparts in other departments, must navigate the stress and challenge of integrating theory with clinical practice. As such, academic emotion regulation can profoundly impact their learning progress and comprehension. Consequently, implementing support strategies that consider emotional aspects and enables nursing students to manage their academic emotions more effectively. To achieve this, developing and applying various programs for academic emotion regulation in extracurricular or curricular courses is necessary. These programs will empower learners to succeed by fostering positive attitudes toward learning. The learning flow score was 3.31, mirroring the findings of Kim and Park [30]. Learning commitment refers to the belief in one's ability to perform a task [11]. Consequently, during COVID-19, as many students were engaging in distance learning, implementing an educational program that promotes learning flow and enables nursing students to perform tasks independently was crucial. Significant findings were obtained for learning flow related to religion, department satisfaction, and GPA. The post-hoc analysis revealed that department satisfaction was above average. GPAs between 3.0 and 4.0 were more common than lower ones. Furthermore, GPAs of 4.0 were more common than those ranging from 3.0 to 4.0. Religion also influenced learning flow. However, additional research is necessary because no prior study has examined the relationship between religion and learning flow. Our post-hoc analysis showed similar results to Yu and Lee's study [10]. Therefore, enhancing course satisfaction to increase learning immersion in online classes is required. Furthermore, to increase course satisfaction, instructor-learner and learner-learner interactions should be actively promoted [20]. These goals are related to systematic curriculum, educational environment, and career direction [31]. Improving the educational environment so students can access various activities and experiences in the online environment through the development of an online curriculum is necessary to establish a customized online progress counseling system and set career direction. The correlation analysis revealed a positive correlation between self-determined learning motivation and stronger academic emotion regulation and immersion. This finding aligns with Kim and Kim's [32] study, which demonstrated a significant positive correlation between self-determined learning motivation and learning commitment. Similarly, Jeon and Yoo [2] found a significant correlation between academic emotion regulation and learning commitment among medical students in an online learning environment. A slight variation was ob-served in the participants’ major subjects, where their self-determined learning motivation, a cognitive factor, and academic emotion regulation were closely related, with immersion being optimized for the situation. This result suggests that the cognitive factor of self-determined learning motivation and the affective factor of academic emotion regulation are not independent. Consequently, academic emotion regulation necessitates that learners manage their emotions constructively according to the learning context. Moreover, the study's results corroborated that self-determined learning motivation directly influences learning flow and indirectly impacts by mediating academic emotion regulation. These findings are consistent with those in the broader literature. For example, Seo and Kim [33] found that academic emotion regulation indirectly affects learning strategies, which subsequently significantly impacts academic achievement. Similarly, Bang [34] discovered it directly influences academic emotion regulation and learning flow. This study confirmed that self-determined learning motivation and academic emotion regulation significantly impact nursing students’ learning flow. Therefore, academic emotion regulation variables, encompassing the learner's emotion awareness, goal-congruent behavior, positive reappraisal of emotions, and approach to emotion regulation strategies, are crucial parameters influencing the learning process and learning flow [10]. Therefore, educational programs to enhance self-determined learning motivation and academic emotional regulation are needed to increase nursing students’ learning engagement. These learners must combine theory and practice in an educational environment that combines non-face-to-face and virtual education. Self-de-termined learning motivation is critical for them. It can be a driving force for them to fulfill their studies and compen-sate for deficiencies independently and thus is an essential learning engagement variable. Therefore, various educational strategies for self-determined learning motivation and academic emotional regulation are necessary to improve competence through nursing students’ learning engagement. These changes will allow them to grow into future professional nurses

CONCLUSION

This study is significant because it confirmed the extent to which self-determined learning motivation, academic emotion regulation, and learning flow are present in nursing students and the mediating role of academic emotion regulation in the relationship between self-determined learning motivation and learning flow. Amidst the rapid transition to online education due to COVID-19 restrictions, academic emotion regulation, encompassing various emotional aspects, emerges as a crucial variable for enhancing learning flow and should be considered when developing appropriate educational programs.

1. Suggestions

The findings indicate that self-determined learning motivation in nursing students affects learning flow through academic emotion regulation. These results are vital for creating effective educational programs and highlight the necessity for further research to validate their efficacy. Additionally, universities preparing future nursing pro-fessionals should be supported by educational policies and measures that foster diverse digital education platforms tailored to the needs of the online learning environment. Since this study focused on nursing students with defined career trajectories, subsequent research should ex-plore different predictors influencing learning flow among college students across various departments and examine the interplay between these factors.

Notes

CONFLICTS OF INTEREST
The authors declared no conflict of interest.
AUTHORSHIP
Conceptualization or/and Methodology - Jo E and Hwang S-J; Data curation or/and Analysis - Jo E; Funding acquisition - Jo E and Hwang S-J; Investigation - Jo E and Hwang S-J; Project admin-istration or/and Supervision - Jo E and Hwang S-J; Resources or/and Software - Jo E and Hwang S-J; Validation - Jo E and Hwang S-J; Visualization - Jo E and Hwang S-J; Writing: original draft or/and review & editing - Jo E and Hwang S-J.
DATA AVAILABILITY
The data that support the findings of this study are available from the corresponding author upon reasonable request.

REFERENCES

1. Jeong CY, Cho EH, Seo YS. The relations of nursing students’ metacognition and learning flow. Journal of Korean Clinical Health Science. 2018; 6(1):1048-1055.

2. Jeon SJ, Yoo HH. Relationship between general characteristics, learning flow, self-directedness and learner satisfaction of medical students in online learning environment. The Journal of the Korea Contents Association. 2020; 20(8):65-74. https://doi.org/10.5392/JKCA.2020.20.08.065
crossref
3. Song CJ. The effect of peer-tutoring program based on the self-determination motivation to learn. Journal of Learner-Centered Curriculum and Instruction. 2017; 17(1):93-120.
crossref
4. Deci EL, Ryan RM. The “what” and “why” of goal pursuits: hu-man needs and the self-determination of behavior. Psychol-ogical Inquiry. 2000; 11(4):227-268. https://doi.org/10.1207/S15327965PLI1104_01
crossref
5. Yang KH, Choi EJ, Park SO, Ko SH, Choi GY, Park JD, et al. Policy support and flexible operation plan for qualitative improvement of clinical practice education of nursing college students. The Journal of Korean Nursing Research. 2020; 4(2):37-51.
crossref
6. Deci EL, Ryan RM. The general causality orientations scale: Self-determination in personality. Journal of Research in Personality. 1985; 19(2):109-134. https://doi.org/10.1016/0092-6566(85)90023-6
crossref
7. Han SM. The relationships between the academic motivation variables, cognitive strategies, and academic achievement. The Korean Journal of Educational Psychology. 2004; 18(1):329-350.

8. Meyer DK, Turner JC. Re-conceptualizing emotion and motivation to learn in classroom contexts. Educational Psychology Review. 2006; 18: 377-390.
crossref
9. Pekrun R. The control-value theory of achievement emotions: assumptions, corollaries, and implications for educational research and practice. Educational Psychology Review. 2006; 18(4):315-341.
crossref
10. Yu JH, Lee SJ. Development and validation of academic emotion regulation scale. The Korean Journal of Educational Psychology. 2012; 26(4):1137-1159.

11. Lofmark A, Wikblad K. Facilitating and obstructing factors for development of learning in clinical practice: a student perspective. Journal of Advanced Nursing. 2001; 34(1):43-50. https://doi.org/10.1046/j.1365-2648.2001.3411739.x
crossref pmid
12. Seok IB. Analyzing characters of the learning flow. Journal of Science Education and Technology. 2008; 24(1):187-212. https://doi.org/10.17232/KSET.24.1.187
crossref
13. Kim EY, Song SY, Choi MG. Structural relationships among the parental autonomy support environment, basic psycho-logical needs, self-determination motivation, and engagement according to types of self-determination motivation. Korean Journal of Youth Studies. 2014; 21(5):1-27.

14. Kim JH. Multiple mediated effect of self-determinative motivation to learn and self-regulated learning strategies on the relation between successful intelligence and learning flow. Journal of Future Oriented Youth Society. 2014; 11(2):43-61.

15. Ryan RM, Connell JP. Perceived locus of causality and in-ternalization: examining reasons for acting in two domains. Journal of Personality and Social Psychology. 1989; 57(5):749-761. https://doi.org/10.1037/0022-3514.57.5.749
crossref
16. Lee EJ. The relations of motivation and cognitive strategies to flow experience. Korean Journal of Education Psychology. 2001; 15(3):199-216.

17. So YH. The effect of academic emotional regulation of academic stress, epistemological belief, and learning flow of 4th and 6th graders. Korean Journal of Child Education. 2013; 22(3):139-154.

18. Bandura A. Social foundation of thought and action: a social cognitive theory. Englewood Cliffs: Prentice Hall; 1986.

19. Bandura A. Perceived self-efficacy in cognitive development and functioning. Journal of Educational Psychology. 1993; 28(2):117-148. https://doi.org/10.1207/s15326985ep2802_3
crossref
20. Kim JM, Sohn KT, Lee EP, Jeong JY, Jang HB, Lee WJ. The effects of interaction between instructor-student and student-student on learning achievement in synchronous e-learning for major classes for university students: the mediating role of learning flow. Journal of Agricultural Education and Human Resource Development. 2020; 52(3):25-48.

21. Park JY, Kang MJ, Park CG, Bae T, Yu SH, Ha J. The effect of self-leadership on nursing students’ learning flow during the Covid-19 pandemic: mediating effects of psychosocial well-being. The Korean Society of School Health. 2022; 35(2):41-48. https://doi.org/10.15434/kssh.2022.35.2.41
crossref
22. Cha YM, Eom WY. Structural relations among autonomy support, self-determination motivation, self-regulated learning ability, and learning flow as perceived by junior college students. The Korean Journal of Thinking Development. 2018; 14(1):27-51.
crossref
23. Bak BG, Lee JU, Hong SP. Reconstructing the classificatory pattern of learning motivation proposed by self-determination theory. The Korean Journal of Educational Psychology. 2005; 19(3):699-717.

24. Jackson SA, Marsh HW. Development and validation of a scale to measure optimal experience: the flow state scale. Journal of Sport and Exercise Psychology. 1996; 18: 17-35. https://doi.org/10.1123/jsep.18.1.17
crossref
25. Kim HJ. Perception of happiness as a learning benefit according to the learning tendencies of adult learners: the mediating effect of self-efficacy and learning flow [dissertation]. Suwon: Ajou University; 2019. p. 1-138.

26. Kim HJ, Song IS. Analysis of the structural relationship if the internal and external factors affecting the learning flow of mid-dle and high school students. The Korean Journal of Educational Psychology. 2013; 27(2):409-429.

27. Lee NY, Han JY. Effects of self-determinative motivation and learning participation on academic adjustment in nursing students. Journal of Korean Association for Learner-Centered Curriculum and Instruction. 2020; 20(2):455-467. https://doi.org/10.22251/jlcci.2020.20.2.455
crossref
28. Noh GO, Kim M. The influence of nursing professionalism and academic emotional regulation on college life adjustment in nursing college students. The Journal of Korean Academic Society of Nursing Education. 2018; 24(4):424-432. https://doi.org/10.5977/jkasne.2018.24.4.424
crossref
29. Tak HY, Cho GP. The differences of learning strategies, academic emotion regulation, and learning attitudes according to level of achievement goal orientation in university students. Journal of Korean Association for Learner-Centered Curricu-lum and Instruction. 2021; 21(2):147-168.
crossref
30. Kim JH, Park MK. Effects of self-determination motivation to learning flow on in self-regulated learning: mediating effect of metacognition. Journal of the Korea Convergence Society. 2018; 9(2):349-357. https://doi.org/10.15207/JKCS.2018.9.2.349
crossref
31. Lee SJ, Lee YH, Heo R. Influencing factors on the major satisfaction of students of department of dental hygiene-focused on Busan. Journal of Korean Society of Oral Health Science. 2013; 1(1):21-31.

32. Kim HW, Kim EY. Structural relationships between pre-early childhood teachers’ perception of distance class, self-directed learning, self-determination motivation and learning engagement in distance class. Journal of Korean Association for Lear-ner-Centered Curriculum and Instruction. 2021; 21(7):787-799. https://doi.org/10.22251/jlcci.2021.21.7.787
crossref
33. Seo EY, Kim EY. The relationship among achievement goal, learning strategy, and academic achievement: the mediating effects of academic emotional regulation. Journal of Learner-Centered Curriculum and Instruction. 2015; 15(10):99-119.

34. Bang HW. The structural relationships among achievement motivation, academic emotion regulation, self-directed learning ability, and learning flow of university students [dissertation]. Busan: Dong-A University; 2019. p. 1-115.