The Mediating Effect of Self-Regulated Learning on the Relationships Among Emotional Intelligence, Collaboration, and Clinical Performance in Korean Nursing Students

Introduction

Social–emotional competence has emerged as an essential professional competency in the 21st century (World Economic Forum, 2016) that facilitates academic achievement and job readiness in college students (Durlak et al., 2011). Furthermore, social–emotional learning in college students is a deliberate process that is influenced by educators (Durlak et al., 2011). Therefore, nursing educators should identify factors related to the social–emotional competence of college students, create an environment suitable for intentionally studying social–emotional competencies, and design experiences to foster social–emotional competence. To achieve these goals, the personal factors of nursing students that influence academic performance must be identified.

Academic achievement for nursing students includes clinical performance, which involves students showing their mastery and integration of learned knowledge as well as psychomotor, decision-making, and interpersonal skills (Scott Tilley, 2008). Through nursing education, students develop the integrated clinical performance ability necessary to resolve patients' health problems as well as the knowledge and skills required to develop as professional nurses. However, earning high grades at nursing colleges does not guarantee high clinical performance as a nurse. Instead, clinical performance is a more appropriate core indicator of academic achievement than university credits, underscoring the importance of clinical importance as a nursing competence.

The ways-of-being model describes people's social–emotional competencies using an interactive and dynamic conceptualization of being that involves three dimensions (ways of feeling, ways of relating to others, and ways of doing), which are based on three layers (identity, awareness, and navigation; Blyth et al., 2017). First, ways of feeling encompass the skills, experiences, and abilities that people use to identify and understand their feelings, including emotional competence (Blyth et al., 2017). Emotional intelligence, which refers to the degree to which one is able to understand, control, and utilize one's own emotions and those of others (Wong & Law, 2002), is associated with clinical performance (M. S. Kim & Sohn, 2019). Therefore, it is necessary to determine whether emotional intelligence affects the clinical performance of nursing students.

Second, ways of relating to others include the abilities required by students to understand, explore, and develop their relationships with others, including behavioral social skills, which involve teamwork, cooperation, and communication (Blyth et al., 2017). Collaboration is a complex intraprofessional and interprofessional process that requires sharing resources and decisions, teamwork, and respect (Emich, 2018). Although collaboration is necessary for all occupations, it is especially crucial for nursing (American Association of Colleges of Nursing, 2016) and is an important leadership competence (Miles & Scott, 2019). For this reason, the impact of collaboration on the clinical performance of nursing students should be explored.

Third, ways of doing incorporate the cognitive skills used to accomplish tasks and goals, ways of feeling, and ways of relating to others (Blyth et al., 2017). Self-regulated learning refers to active involvement of the learner in his or her own metacognitive, motivational, and behavioral learning process (Panadero, 2017). Through self-regulated learning, students activate and sustain the use of knowledge using strategic monitoring and modulation of their affect, cognition, and behavior to achieve their educational goals (Schunk & Zimmerman, 2012). Self-regulated learning is a social–emotional competence that reflects a way of doing because it refers to a self-regulation strategy in which emotions, ways of feeling, and ways of relating to others are integrated for the goal of academic achievement. Self-regulated learning is relatively strongly correlated with academic achievement and is predictive of student achievement regardless of the type of learning task (Mega et al., 2014; Schunk & Zimmerman, 2012). Furthermore, it is an actual facilitator and mediator of academic achievement (Schunk & Zimmerman, 2012). Therefore, it may be assumed that learners with the social–emotional competencies of emotional intelligence and collaboration achieve academic performance through the mediating role of self-regulated learning.

Associations between higher levels of emotional intelligence and, respectively, higher clinical performance (M. S. Kim & Sohn, 2019) and academic performance (Foster et al., 2017) have been reported. Moreover, collaboration has been reported to be associated with clinical competence (Stone et al., 2013), academic achievement has been shown to be correlated with self-regulated learning (Ergen & Kanadli, 2017), and the self-regulated learning ability of nursing students has been shown to affect academic performance in basic nursing practicum settings (Hsu et al., 2009). However, the degree to which self-regulated learning is associated with clinical performance in nursing students is an issue that has yet to be examined in the literature.

In this study, the ways-of-being approach to social–emotional learning and the findings of previous studies are employed to predict clinical performance in nursing students. Emotional intelligence, collaboration, and self-regulated learning were selected as the factors belonging, respectively, to the emotional, relational, and behavioral domains of social–emotional learning, whereas clinical performance was analyzed as a component of academic achievement. The aim of this study was to explore self-regulated learning as a mediator in the relationship between the respective variables of emotional intelligence and collaboration and clinical performance in nursing students.

Hypothesis

Emotional intelligence and collaboration simultaneously affect clinical performance directly and indirectly via a mediator in nursing students. On the basis of this, the following four hypotheses were formulated in this study:

Hypothesis 1: Higher emotional intelligence relates to higher clinical performance. Hypothesis 2: Higher collaboration relates to higher clinical performance. Hypothesis 3: Self-regulated learning mediates the relationship between emotional intelligence and clinical performance. Hypothesis 4: Self-regulated learning mediates the relationship between collaboration and clinical performance. Methods Study Design

A cross-sectional survey was conducted to investigate the relationships among emotional intelligence, collaboration, and clinical performance and the mediating role of self-regulated learning in a population of Korean nursing students.

Participants

The participants were nursing students in their third or fourth year of a bachelor's degree program who had at least one semester of clinical training and who provided written informed consent to participate. Of the 305 nursing students enrolled, 302 completed the questionnaire (response rate: 99%).

A sample size of 200–400 is widely recommended for testing models designed using structural equation modeling, whereas a sample size of 100–200 is required for testing latent variable mediation models (Hoyle et al., 1999). Thus, the sample size used in this study was sufficient for the analysis.

Instruments Emotional intelligence

Emotional intelligence was evaluated using the Korean version (Jung & Jung, 2007) of the Emotional Intelligence Scale (Wong & Law, 2002), which has been widely used to measure emotional intelligence in Korean nursing students. This scale contains four subcategories: (a) appraisal and expression of emotion in oneself (four items), (b) appraisal and recognition of emotion in others (four items), (c) regulation of emotion (four items), and (d) use of emotion (four items). Each item is scored using a 7-point Likert scale (1 = strongly disagree, 7 = strongly agree), with higher scores corresponding to higher emotional intelligence. Mean subcategory and total scale scores were used in this study. The Cronbach's alpha coefficient for this scale was .91 in this study.

Collaboration

Collaboration was evaluated using a previously published collaboration scale (M. J. Chung & Chang, 2012) comprising the two subcategories of interpersonal teamwork (eight items) and functional teamwork (nine items), with each item scored using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree) and higher scores indicating higher levels of collaboration. The mean scores for the subcategories and the overall collaboration scale were used. The Cronbach's alpha coefficient for this scale was .95 in this study.

Self-regulated learning

Self-regulated learning was evaluated in this study using the Self-Regulated Learning Scale (M. K. Chung, 2005). This scale is a higher-order construct consisting of three dimensions (motivational, cognitive, and behavioral self-regulated learning), each of which includes four subcategories. The subcategories of motivational self-regulated learning are self-efficacy (11 items), internal value (10 items), test anxiety (six items), and external goal orientation (five items); those for cognitive self-regulated learning are cognitive strategy (13 items), rehearsing and memorizing (seven items), inspection (six items), and planning (five items); and those for behavioral self-regulated learning are effort control (eight items), time and study control (seven items), seeking help (six items), and study environment control (four items). Each item is scored using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree), with higher scores corresponding to higher levels of motivational, cognitive, and behavioral self-regulated learning, respectively. The mean scores for the subcategories and the overall motivational, cognitive, and behavioral self-regulated learning scales were calculated. The Cronbach's alpha coefficients for the overall scale and the motivational, cognitive, and behavioral self-regulated learning dimensions were .96, .89, .93, and .90, respectively, in this study.

Clinical performance

Clinical performance was evaluated using the Clinical Performance Scale (Lee et al., 1990), which consists of five subscales: nursing process (11 items), nursing skill (11 items), teaching/coordinating (eight items), interpersonal relationship/communication (six items), and professional development (nine items). Each item is scored using a 5-point Likert scale (1 = I can't do it at all, 5 = I do it very well), with higher scores indicating higher clinical performance. The mean scores for the subscales and the overall scale were used. The Cronbach's alpha coefficient for this scale was .95 in this study.

Data Collection and Ethical Considerations

The study was conducted after approval was received from the institutional bioethics review board of Daegu Catholic University (Approval No. CUIRB-2019-0024). Data collection was conducted from September to October 2019 at five nursing departments located, respectively, in three metropolitan cities and two medium-sized cities. The head of each nursing department assisted the researcher with data collection. Assistant researchers recruited participants by posting promotional posters for nursing college students on online and/or offline bulletin boards regularly used by the nursing departments. Those students who expressed interest in participating were asked to gather in one classroom to complete the questionnaire survey at a predetermined time. The participants were provided explanations by a research assistant about the purpose of the study, the methods used to collect data, guarantees of confidentiality and anonymity, their right to refuse to consent and to withdraw, the benefits and disadvantages of participating (including the lack of any possible negative impact on their grades), and the storage and disposal of study data. After the participants voluntarily signed the consent form, they began completing the questionnaire survey and were given a gift worth approximately 3.5 U.S. dollars.

Data Analysis

A descriptive analysis using SPSS Version 25.0 (IBM Corp., Armonk, NY, USA) was conducted to summarize the participants' demographic characteristics. Means and standard deviations of the variables were computed, as well as Pearson correlation coefficients and Cronbach's alpha.

Using SmartPLS Version 3.0 (Ringle et al., 2015), partial least squares structural equation modeling (PLS-SEM) was performed to validate the measurements and the proposed hypotheses. PLS-SEM was used because it is appropriate for exploring new ideas and verifying self-regulated learning as a mediator in the relationships between emotional intelligence and collaboration, respectively, and clinical performance. PLS-SEM also allows higher-order models to be verified. In this study, self-regulated learning was conceptualized as a three-dimensional construct explained by motivational, cognitive, and behavioral self-regulated learning. A two-step procedure was followed to assess the measurement and structural models.

In the first step, the suitability of the measurement model was evaluated in terms of reliability (indicator and internal consistency) and validity (convergent and discriminant). The indicator loadings of all latent variables were checked to evaluate indicator reliability. An indicator loading of .4 or greater is appropriate, and inappropriate indicators should be removed (Hair et al., 2016). Internal consistency reliability was evaluated using Cronbach's alpha coefficients and composite reliability. The threshold value for composite reliability is between .6 and .95 (Hair et al., 2016). Average variance extracted (AVE) was used to assess convergent validity. The AVE threshold value should be above .5 (Hair et al., 2016). To evaluate discriminant validity, the square root of the AVE for each construct was compared with its correlations with all other constructs (Fornell & Larcker, 1981). Discriminant validity was confirmed if the correlations of a construct with all other constructs were less than the square root of its AVE (Fornell & Larcker, 1981).

In the second step, collinearity was evaluated using the variance inflation factor (VIF) before evaluating the structural model, with a VIF of 5 or more indicating collinearity (Hair et al., 2016). The structural model was evaluated using significant coefficients (t value), the coefficient of determination (R2), the cross-validated redundancy measure (Q2), and the effect size (F2). R2 threshold values of .25, .50, and .70 denote weak, moderate, and strong coefficients of determination, respectively (Hair et al., 2016). In SmartPLS, the relationships between the constructs were determined by examining path coefficients and t statistics using bootstrapping procedures. Q2 denotes the predictive relevance of a structural model (Hair et al., 2016). F2 is a measure of the strength of the relationship between constructs and represents the degree to which an exogenous construct is relevant as a predictor for an endogenous construct. F2 values of .02, .15, and .35 are considered to be low, moderate, and high, respectively (Hair et al., 2016).

For bootstrapping in a two-step procedure, the Preacher and Hayes procedure was used in this study to test the mediating effects (Preacher & Hayes, 2008). First, the direct effects were verified using bootstrapping without the mediator. Second, after including the mediator in the model, the path coefficients, the t values, and the bias-corrected 95% confidence interval were calculated to confirm the significance of the indirect effects. If the indirect effects were found to be significant, the strength of the mediator was then examined using total effects and variance accounted for (VAF). VAF is the indirect effect divided by the total effect, with VAF values exceeding .2 and .8 considered to indicate partial and full mediation, respectively.

Results

The average age of the nursing students in this study was 22.3 years (SD = 2.05). Most (81.1%) were women, and 50.3% were fourth-year students. Almost half (49.7%) had completed one semester of clinical training, 40.7% had completed three training sessions, and 9.6% had completed four or more semesters. Their average nursing aptitude score was 70.6 (SD = 18.66), and their average score for academic achievement in the previous semester was 71.46 (SD = 17.78).

As shown in Table 1, the mean score was 3.67 (SD = 0.45) for clinical performance, 5.23 (SD = 0.75) for emotional intelligence, and 4.01 (SD = 0.53) for collaboration. The mean score was 3.45 (SD = 0.42) for self-regulated learning, 3.33 (SD = 0.42) for motivational self-regulated learning, 3.59 (SD = 0.50) for cognitive self-regulated learning, and 3.46 (SD = 0.52) for behavioral self-regulated learning.

Table 1. - Measurement Model (N = 302) Construct Mean SD Indicator Loadings Cronbach's α Composite Reliability AVE Clinical performance 3.67 0.45 .88 .91 .68  Nursing process 3.61 0.52 .80  Nursing skill 3.64 0.55 .84  Teaching/coordinating 3.71 0.57 .83  Interpersonal relationship/communication 3.79 0.60 .85  Professional development 3.68 0.52 .81 Emotional intelligence 5.23 0.75 .76 .85 .58  Appraisal and expression of emotion in oneself 5.42 0.94 .75  Appraisal and recognition of emotion in others 5.50 0.93 .75  Regulation of emotion 5.15 0.99 .71  Use of emotion 4.84 1.08 .84 Collaboration 4.01 0.53 .90 .95 .91  Interpersonal teamwork 3.99 0.55 .95  Functional teamwork 4.04 0.56 .96 Self-regulated learning a 3.45 0.42 .89 .91 .51  Self-efficacy .67  Internal value .72  Cognitive strategy .77  Rehearsing and memorizing .78  Inspection .74  Planning .62  Effort control .73  Time and study control .71  Seeking help .73  Study environment control .63 Motivational self-regulated learning 3.33 0.42 .70 .87 .77  Self-efficacy 3.24 0.58 .87  Internal value 3.68 0.62 .89 Cognitive self-regulated learning 3.59 0.50 .81 .87 .63  Cognitive strategy 3.55 0.57 .81  Rehearsing and memorizing 3.73 0.60 .81  Inspection 3.75 0.65 .84  Planning 3.26 0.78 .72 Behavioral self-regulated learning 3.46 0.52 .77 .85 .59  Effort control 3.72 0.65 .78  Time and study control 3.22 0.72 .81  Seeking help 3.28 0.60 .76  Study environment control 3.59 0.74 .72

Note. AVE = average variance extracted.

a Higher-order construct.


Measurement Model

The indicator loadings for test anxiety and external goal orientation (within motivational self-regulated learning) were less than .40 (the threshold value). Furthermore, the Cronbach's alpha of motivational self-regulated learning was .41, its composite reliability was .68, and its AVE was .42, all of which were lower than the threshold values. Therefore, these two indicators were deleted.

As shown in Table 1, all indicator loadings were .62 or higher. The Cronbach's alpha coefficients were higher than .70 for all of the constructs, and the composite reliability was between .85 and .95. Therefore, internal consistency reliability was confirmed.

The AVE was above .51, showing satisfactory convergent validity for the measurement model (Table 1). For discriminant validity testing, the correlations between variables were compared using the square root of the AVE of each variable. The correlation between clinical performance and emotional intelligence was .40, that between clinical performance and collaboration was .53, and that between clinical performance and self-regulated learning was .54. The correlation between emotional intelligence and collaboration was .46, and that between emotional intelligence and self-regulated learning was .49. The correlation between collaboration and self-regulated learning was .48. The square roots of the AVE for clinical performance, emotional intelligence, collaboration, and self-regulated learning were .83, .76, .95, and .71, respectively. The correlation of each construct with all other constructs was less than the square root of its AVE, confirming discriminant validity.

Structural Model

The VIF was less than 5, indicating the absence of collinearity. The significance of each coefficient, the t value, and the respective amounts of variance explained by R2, Q2, and F2 are presented in Tables 2 and 3.

Table 2. - PLS Path Model Without Mediator (N = 302) Construct VIF R 2 Q 2 F 2 Clinical performance .31 .20 Emotional intelligence 1.27 – – .04 Collaboration 1.27 – – .23 Path β t Bias-Corrected 95% CI Emotional intelligence → clinical performance .19*** 3.39 [0.07, 0.29] Collaboration → clinical performance .45*** 8.05 [0.34, 0.55]

Note. PLS = partial least squares; VIF = variance inflation factor; R2 = coefficient of determination; Q2 = predictive relevance; F2 = effect size; β = path coefficient.

***p < .001.


Table 3. - Mediating Effect of Self-Regulated Learning on the Relationships Among Emotional Intelligence, Collaboration, and Clinical Performance (N = 302) Construct VIF R 2 Q 2 F 2 Clinical performance .39 .26 Emotional intelligence 1.44 – – .01 Collaboration 1.42 – – .13 Self-regulated learning 1.48 .33 .16 .14  Motivational self-regulated learning 1.00 – – –  Cognitive self-regulated learning 1.00 – – –  Behavioral self-regulated learning 1.00 – – – Path Direct Effects Indirect Effects Total Effects VAF β t β t β t Emotional intelligence → clinical performance .07 1.31 a .12*** 4.64 b .19** 3.34 .62 Collaboration → clinical performance .33*** 5.88 c .11*** 4.14 d .45*** 8.15 .25 Self-regulated learning → clinical performance .35*** 6.99 Emotional intelligence → self-regulated learning .34*** 6.62 Collaboration → self-regulated learning .33*** 5.82

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