Emotional intelligence (EI) and self‐directed learning: Examining their relation and contribution to better student learning outcomes in higher education
Abstract
Self‐directed learning is recognised as a key goal of higher education. To facilitate self‐directed learning, emotional intelligence (EI), which encompasses the ability to regulate one's own emotions and to generate positive emotions, is important. The present study aimed to examine the effects of EI on self‐directed learning and how EI and self‐directed learning contribute to key learning outcomes in higher education, including grade point average (GPA), generic learning outcomes (including social, cognitive and self‐growth outcomes) and students’ satisfaction with their university experience. The study adopted a prospective longitudinal design with 560 first‐year undergraduate students completing different measures at the beginning and end of the academic year. Results of the structural equation modelling showed that EI had a strong effect on self‐directed learning, which in turn was positively associated with GPA and various generic learning outcomes that are related to students’ satisfaction with the university experience. To better delineate the relationship between EI and self‐directed learning, multiple regression was performed. Results indicated that three key emotional abilities—emotional regulation of the self (ERS), appraisal of emotions in the self (AES) and emotional regulation of others (ERO)—were positively associated with self‐directed learning. This study provided empirical evidence that students who are more emotionally intelligent are more self‐directed, leading to higher achievement in both academic and generic development, which in turn results in higher university satisfaction. Implications of the findings are discussed.
Introduction
With technological advancement, information and massive data can be relayed instantly, accelerating the growth of knowledge and change in society. In response to the rapid pace of change in modern society, self‐directed learning has been deemed an important educational aim in many countries, such as Hong Kong, Taiwan, Korea, Japan and Thailand (OECD, 2000; Mok et al., 2007). In the context of higher education, self‐directed learning has also been fostered greatly. In addition to its relevance in meeting the needs of society, self‐directed learning is highly valued in higher education because its emphasis on personal autonomy, personal responsibility and personal growth embodies the core values of higher education (Wilcox, 1996). Hence, other than self‐directed learning, other similar concepts such as autonomous learning and lifelong learning are emphasised and designated as generic graduate attributes in many universities all over the world (Chemers et al., 2001; Macaskill & Denovan, 2013).
In Hong Kong, similar to many other countries, an important mission of higher education is to nurture globally competitive students equipped with the capacity for lifelong learning, so as to cope with the challenges of a more dynamic future (University Grants Committee, 2010). In relation to this, there was a revamp of the university system in Hong Kong. More specifically, since September 2012, universities have moved from a 3‐year university system favouring early specialisation within a discipline to a 4‐year system that allows disciplinary study to be grounded in a broader knowledge base that encourages critical thinking and self‐directed learning (Curry, 2012). The new 4‐year undergraduate curriculum not only provides more diverse learning experiences for students—such as academic exchange, internship and community service—but also, more importantly, provides an inclusion of the common core courses, which encourage inquiry‐based learning.
While self‐directed learning has received extensive attention from educators and researchers, there is a paucity of research that investigates factors that are facilitative of self‐directed learning, especially in the higher education context (Song & Hill, 2007; Macaskill & Denovan, 2013). The present study aims to fill this gap by examining the role of emotional intelligence (EI) in self‐directed learning in higher education. Indeed, the process of becoming a self‐directed learner is a painful one, as students have to experience negativity, confusion, frustration and dissatisfaction with their learning experience when they enter a new learning experience (Lunky‐Child et al., 2001). While some may react to negative emotions by choosing to withdraw, there are others who are able to override them and respond assertively, to invest more effort in solving the problem. That said, EI is likely to play an influential role in the process of self‐directed learning. The aim of the present study was to examine the effects of EI on self‐directed learning and how EI and self‐directed learning contribute to key learning outcomes in higher education, including academic achievement, generic learning outcomes (i.e. social, cognitive and self‐growth outcomes) and students’ satisfaction with their university experience.
Emotional intelligence
Emotional intelligence refers to individuals’ ability to appraise, express and regulate emotions in the self and others and to make use of these emotions for problem solving (Salovey & Mayer, 1990). According to Salovey and Mayer (1990), EI is conceptualised as encompassing three major categories of emotional abilities. The first dimension involves the appraisal and expression of emotions in the self and others (i.e. the ability to accurately perceive and express the emotions of the self and others, enabling socially adaptive behaviours). The second dimension involves the regulation of emotions in the self and others (i.e. the ability to control and modify the emotional states of the self and others, so as to meet particular goals). The third dimension refers to the utilisation of emotions in adaptive ways (i.e. the ability to make use of one's own emotions for solving problems). Although the model was later refined by Mayer and Salovey (1997), the two models are similar in their basic components of EI (Schutte et al., 2009).
While there are different theoretical conceptualisations of EI in the field, Salovey and Mayer's (1990, 1997) ability model of EI is one of the most widely accepted models in the field, with the most research generated in peer‐reviewed journals (Zeidner et al., 2002; Fernandez‐Berrocal & Extremera, 2006; Fernandez‐Berrocal et al., 2012).
Self‐directed learning
What is self‐directed learning?
Among the varied definitions of self‐directed learning, Knowles's (1975) definition is one of the most widely adopted in the literature (O'Shea, 2003). It defines self‐directed learning as ‘a process in which individuals take the initiative, with or without the help of others, in diagnosing their learning needs, formulating learning goals, identifying human and material resources for learning, choosing and implementing appropriate learning strategies and evaluating learning outcomes’ (p. 18). Despite its popularity, this process perspective of self‐directed learning was criticised, as its emphasis on the skills and abilities required by an individual in the learning process cannot ensure one's persistence in learning throughout life (Macaskill & Denovan, 2013).
In view of the above, the personality perspective of self‐directed learning is adopted in this study, which conceptualises self‐directed learning as a personal attribute or learner's characteristic. More specifically, we adopt the perspective of Brockett (1983) in this study, which defines self‐directed learning as a disposition to engage in learning activities where the individual takes personal responsibility for developing and carrying out learning endeavours in an autonomous manner without being prompted or guided by other people, such as teachers, parents or peers. Indeed, as pointed out by Oddi (1987), persistence is a psychological variable, which is not dependent upon skills. The quintessence of self‐directed learning is a willingness to initiate and maintain systematic learning based on personal initiative. Hence, self‐directed learners are those with strong willpower to follow through and exercise self‐restraint and self‐discipline to realise their plans and goals (Candy, 1991). That said, self‐directed learning is not so much about methods of learning, but about developing capabilities in students to enable them to become autonomous learners (Macaskill & Denovan, 2013).
The term ‘self‐directed learning’ is used interchangeably with a number of educational concepts in the literature, such as autonomous learning, self‐regulated learning and lifelong learning (Hiemstra, 2000; Svedberg, 2010). While autonomous learning can be considered synonymous to self‐directed learning, self‐regulated learning and lifelong learning are concepts that are not the same as self‐directed learning (Macaskill & Taylor, 2010). To illustrate, for autonomous learning, the learner accepts responsibility for his or her own learning and is able to take charge of what is to be learnt as well as when and how to learn it (Chene, 1983; Chan, 2001). Hence, autonomous learning shares the same fundamental principle of self‐directed learning (i.e. the locus of control and the responsibility for learning lie in the hands of the learner) (Pierson, 1996). Meanwhile, for self‐regulated learning, although it appears highly similar to self‐directed learning, there is a distinctive difference between the two (Pilling‐Cormick & Garrison, 2007; Loyens et al., 2008). As explained by Jossberger et al. (2010), self‐directed learning is situated at the macro level, which concerns the planning of the learning trajectory as a whole, since the self‐directed learner is always the one to decide what is to be learned and how best to accomplish it. In contrast, self‐regulated learning is a more micro‐level process, concerning the processes within task execution, whereby the learner is involved in monitoring, regulating and controlling their own cognition, motivation and behaviour in the learning process. Self‐directed learning, therefore, is a broader concept than self‐regulated learning. As to lifelong learning, self‐directed learning is indeed a prerequisite for this (Greveson & Spencer, 2005). As explained by Candy (1991), self‐directed learning and lifelong learning have a reciprocal relationship. Given that self‐directed learning is the principal activity in the independent pursuit of learning, lifelong learning is ‘equipping people with skills and competencies to continue their own “self‐education” beyond the end of formal schooling’ (Candy, 1991, p. 15). Self‐directed learning, therefore, can be considered both a means and an end to lifelong learning.
Self‐directed learning in higher education
Given that the ultimate aim of self‐directed learning is to facilitate students to become autonomous and independent learners, there is a shift that moves away from teacher‐centred learning to student‐centred learning, underpinned by a move from large lectures towards the use of small‐group teaching and learning (Smith, 2016). For instance, problem‐based learning is one of the main approaches in higher education to encourage self‐directed learning in students. For problem‐based learning, small groups of students learn and work collaboratively to understand and solve various content‐related problems that are presented in a case format (Cockrell et al., 2000). During the process, students have to select and study the relevant information and literature for the issues generated and to plan and monitor the study activities that need to be carried out. Then, they have to share and critically evaluate their findings and to elaborate on knowledge acquired, if necessary. Hence, problem‐based learning not only helps students to construct an extensive and flexible knowledge base, but also helps them to become effective collaborators and develop effective problem‐solving and self‐directed learning skills (Loyens et al., 2008).
Self‐directed learning and student learning outcomes
There is accumulating research evidence on the positive relationships between self‐directed learning and various student learning outcomes. For example, Lounsbury et al. (2009) reported that self‐directed learning was significantly correlated with the cumulative grade point average (GPA) for college students and a number of cognitive ability measures, including the ACT college entrance examination test, verbal reasoning, numerical reasoning, abstract reasoning and overall reasoning. Similarly, Zhoc and Chen (2016) found that self‐directed learning was positively associated with students’ GPA and public examination results of the Hong Kong Advanced Level Examination (HKALE) and the Hong Kong Diploma of Secondary Education (HKDSE). Indeed, the experience and opportunity to engage in self‐directed learning activities were found to enhance students’ confidence, intrinsic motivation to learn, critical thinking, quality of understanding as well as retention and recall (Fry, 1972; Lunky‐Child et al., 2001; Jennings, 2007; Smedly, 2007).
Relationship between EI and self‐directed learning
‘Emotion is the foundation of learning’ (Zull, 2006, p. 7). It has a role to play in the process of learning, including self‐directed learning (Rager, 2009). Emotion, however, is a double‐edged sword, which can serve as a motivator to enhance learning, but can also prevent one from learning effectively. EI, which involves the ability to manage emotions, can therefore make a critical difference to students’ learning and academic performance.
More specifically, the relationship between EI and self‐directed learning could be unravelled from McCombs and Whisler's (1989) analysis regarding the role of affective variables in autonomous learning. According to McCombs and Whisler (1989):
The propensity of learners for autonomous learning is a function of the development of cognitive and metacognitive abilities for (a) processing, planning and regulating learning activities; and (b) controlling and regulating affect and motivation… If learners are to apply processes necessary for autonomous learning, they must generate positive affect and motivation toward the learning task and toward applying the mental effort required. (p. 277)
The analysis by McCombs and Whisler reveals the significance of two elements in driving autonomous or self‐directed learning: (i) self‐regulation and control of affect; and (ii) generation of positive affect and motivation, which are both core components of EI.
Self‐regulation and control of affect
Self‐directed learning involves processing, planning and regulating learning activities. In the process of self‐directed learning, self‐regulation of affect is particularly important as students are required to suppress distractions and other short‐term attractions so as to sustain their focus and effort on tasks in order to meet their self‐defined learning goals (Pintrich & De Groot, 1990; Fredricks et al., 2004). Moreover, self‐regulation of affect is significant to avoid ruminating on negative events during times of frustration. In so doing, it helps to maintain effort and persistence to act on progressing towards the learning goals. The capability to regulate negative emotions also provides an adaptive mechanism for students to handle stressful academic learning (Saklofske et al., 2012). That said, it is functional in helping students to become more emotionally stable, which helps to minimise the adverse effects of negative emotions on cognitive functioning for academic learning (Perera & DiGiacomo, 2013).
Furthermore, EI is at the core of self‐regulation (Mayer & Salovey, 1993; DeSteno & Salovey, 1997; Green & Salovey, 1999). It is related to the fact that the emotional abilities of EI to monitor one's own and others’ emotions, as well as to use this information in the process to guide thinking and action, are facilitative of self‐reflection and self‐monitoring, which are important for self‐regulation (Lambert & McCombs, 1998; Kanfer & McCombs, 2000). When an individual is more aware of positive or negative emotions through the processes of self‐reflection and self‐monitoring, attention is turned towards the self before taking action. Hence, self‐regulation has a self‐referenced origin as an individual evaluates value to self, based on feedback from the emotions, that facilitates action to regulate oneself in achieving goals (Salovey, 1992).
Generation of positive affect and motivation
Motivation is one tenet that is recognised as central to the concept of self‐directed learning. Motivation not only drives the decision to participate, but also helps to sustain the will to work through a task to the end, so that goals are achieved (Corno, 1992).
According to Salovey and Mayer (1990), individuals who are more emotionally intelligent are better at utilising emotions to motivate themselves to achieve a worthwhile end. In the realm of learning, they may harness positive emotions to foster their intrinsic motivation to learn and to increase their confidence in their capabilities to motivate goal‐oriented behaviour. As suggested by Mega et al. (2014), students’ positive emotions can enhance their belief in the incremental theory of intelligence (i.e. conceiving intelligence as changeable and increasable), which thus motivates the enhancement of intellectual abilities through effort and learning. Moreover, positive emotions also enhance students’ belief in their own intelligence, which can have a motivating effect on learning (i.e. making more effort to understand their academic work, as well as planning, monitoring and regulating their academic study) (Seifert, 2004). More importantly, affective state can affect students’ goal adoption, with students experiencing positive emotions tending to feel that they have the resources to approach a certain goal, which in turn facilitates goal‐approaching behaviour. In contrast, students experiencing negative emotions tend to perceive themselves as having no resources to approach a particular goal, and may therefore withdraw from goal engagement (Linnenbrink, 2007). While approach motivation was found to be associated with higher academic performance, avoidance motivation was found to be related to lower academic achievement (Huang, 2012).
EI and student success
In higher education, there is wide agreement that student success should include not only traditional measures of academic achievement, but also the attainment of desired student and personal development outcomes (also referred to as generic learning outcomes, such as communication skills, critical thinking, creativity and lifelong learning), as well as the degree to which students are satisfied with their college experience (Kuh et al., 2006).
EI and academic performance
Academic performance is a traditional measure of student success in higher education and there is increasing evidence supporting the positive relationship between EI and academic performance. For example, studies have shown the significant association between EI and academic performance, such as GPA and examination results (O'Connor & Little, 2003; Fabio & Palazzeschi, 2009; Hogan et al., 2010). Students who were academically successful were found to have significantly higher EI than the unsuccessful students (Parker et al., 2004, 2005). Indeed, EI was found to predict academic performance after controlling for general mental abilities and personality traits (Song et al., 2010). In a study conducted by Sanchez‐Ruiz et al. (2013), they even found that EI was able to predict academic performance over and above cognitive ability and established personality traits. Above all, based on a meta‐analytic study that involved 8,700 participants, EI was found to have modest to moderate validity in predicting academic performance (Perera & DiGiacomo, 2013).
EI and the development of generic learning outcomes
Beyond academic performance, generic learning outcomes are considered important measures of student success as they enable individuals to successfully navigate the working world and meet the needs of a rapidly changing and knowledge‐intensive economy (Bridgstock, 2009). Generic outcomes (also referred to as graduate attributes and generic skills) are the qualities, skills and abilities that students are expected to develop during their time at university. These go beyond disciplinary expertise or technical knowledge, and help to prepare graduates for their unknown future (Bowden et al., 2000; Barrie, 2007).
In this regard, EI also contributes to students’ development of generic outcomes, including social, cognitive and self‐growth outcomes. The main reason is that EI facilitates social interaction in an increasingly interactive and collaborative learning environment in higher education. In fact, there is accumulating evidence on the significant association between EI and positive social relationships (Lopes et al., 2003, 2004). One attributable reason is that ability in emotional perception was related to greater accuracy in the assessment of mood experienced by others, which in turn was pertinent to successful interactions with others (Engelberg & Sjoberg, 2004). Moreover, individuals with higher EI were able to make use of the emotional contents of their experiences to develop greater attachment to others, building positive social relationships (Clarke, 2010). Beyond this, EI is associated with positive emotions that facilitate social interactions; the expression of positive emotions tends to elicit favourable responses from others, but the expression of negative emotions tends to drive people away (Furr & Funder, 1998). Hence, individuals who are unable to create positive emotions were found to have greater difficulty in developing and sustaining even casual interpersonal relationships (Ekman, 1992).
In this relation, EI is positively related to the development of cognitive outcomes (e.g. critical and analytical thinking, problem solving and viewing things from a broader perspective) as EI contributes to positive interactions with peers and teachers, which are found to be crucial for general cognitive growth (Pascarella & Terenzini, 2005). More specifically, gains in cognitive development are linked with activities such as studying or working on group projects with peers, exposure to people with diverse backgrounds and perspectives, and discussing subject matters or issues related to their studies with peers and teachers (Kuh et al., 2006). Similarly, for social outcomes (e.g. communication skills, leadership and teamwork), interactions with peers and teachers, as well as participation in educational out‐of‐class experiences (e.g. work experience, residential hall living experience and meaningful leadership activities) are all significant (Astin, 1993; Kuh, 1995; Pascarella & Terenzini, 2005). As to the self‐growth outcomes (such as time management and critical self‐reflection), peer interactions, voluntary work, community service and experience of serving in student organisations are all positively related (Kuh, 1995; Astin et al., 1999). To facilitate self‐growth, critical reflection is an essential element, which is motivated and prompted by the intensity of emotions experienced. Hence, individuals with higher EI are more aware of emotions in the self and others, facilitating more critical reflections and thereby fostering self‐growth (Clarke, 2010).
EI and students’ satisfaction with their university experience
In addition to academic achievement and generic learning outcomes, there is a need to complement student success with student satisfaction with their university experience, which refers to the degree to which students are satisfied with their experience in the learning environment (Kuh et al., 2006; Tessema et al., 2012). Student satisfaction is an important attribute or goal of the overall collegiate experience, and hence a key outcome of higher education (Astin, 1993; Grayson & Meilman, 1999). More importantly, it is an important outcome variable that is positively associated with other dimensions of student success (e.g. academic performance, levels of student engagement and social integration) (Strauss & Volkwein, 2002; NSSE, 2005).
There are no previous studies that investigate the direct relationship between EI and students’ satisfaction with their university experience. Yet, the study conducted by Lounsbury et al. (2005) may help to shed light on the relationship between the two, finding as it did that there is no obvious relationship between emotional stability and college satisfaction. Since EI is associated with emotional stability, individuals with high EI are better at regulating their emotions and hence are more emotionally stable (Salovey et al., 1999). Therefore, it is believed that there is also no direct relationship between EI and students’ satisfaction with their university experience. Instead of EI, Lounsbury et al. (2009) found that self‐directed learning leads to positive‐valued outcomes, such as GPA, which in turn increase college satisfaction. This finding is consistent with the previous result reported by Lounsbury et al. (2005) (i.e. higher GPA would lead to higher levels of college satisfaction). In a similar vein, gains in generic outcomes (including social, cognitive and self‐growth outcomes) should lead students to be more satisfied with their college experience.
The present study
The aim of the present study was to examine the role of EI on self‐directed learning and how both EI and self‐directed learning contribute to key learning outcomes in higher education, including both the academic (i.e. GPA) and generic learning outcomes (i.e. social, cognitive and self‐growth outcomes), as well as students’ satisfaction with the university experience. Specifically, it was postulated that while EI and self‐directed learning both directly influence the various student learning outcomes, EI also influences self‐directed learning, which in turn enhances the different student learning outcomes. That said, self‐directed learning would be partially mediating the relationship between EI and the various student learning outcomes.
The study is significant as it provides empirical evidence on the value of EI and self‐directed learning in promoting student success. Given that EI has been shown to be modifiable through training or intervention (Di Fabio & Kenny, 2011; Vesely et al., 2014), the study has practical implications on how to enhance the key learning outcomes in higher education, including students’ academic performance, generic outcomes and their satisfaction with the university experience. It can also help shed light on how best to improve the quality of undergraduate education, especially since the early experiences in higher education are critical in establishing values, attitudes and approaches to learning that will promote success for and beyond tertiary education (McInnis & James, 1995; Trautwein & Bosse, 2017).
Apart from the above, the study contributes to expand our understanding of self‐directed learning studied in the eastern context (a large body of research on self‐directed learning has been conducted mainly in the western context, which casts doubt on the applicability of this concept in the eastern context) (Kramsch & Sullivan, 1996; Chan et al., 2002). In fact, students in Hong Kong are generally perceived to be dependent, reticent and passive (Pierson, 1996; Chan, 2001). Before the introduction of the New Senior Secondary Curriculum (NSSC) in 2009, the old curriculum was criticised as being largely examination oriented, emphasising passive learning. As a result, students under the old curriculum were used to learning in a teacher‐centred approach, with teachers having great control over the learning process. As such, students tended to see knowledge as something to be transmitted by the teachers, rather than as something to be explored and discovered on their own. Their passivity in learning could also be reflected in their preference for memorisation and regurgitation of information, an adaptive strategy for coping with assessment demands. To address this problem, the NSSC was developed based on the guiding principle of developing ‘students’ overall capacities for self‐directed, lifelong learning’ (Education and Manpower Bureau, 2005, p. 20). In addition to increasing students’ competence in critical thinking and independent learning, its ultimate purpose was to nurture students to become self‐directed lifelong learners. In other words, self‐directed learning is a timely issue that is greatly applicable in the local context.
Method
Participants
A total of 560 first‐year undergraduate students from 10 faculties of a university in Hong Kong responded to the survey at two time points of the study. Among them, there were 213 males (38.0%) and 346 females (61.8%) [1 unreported (0.2%)]. Their ages ranged from 17 to 25 (M = 18.35, SD = 1.10). All of the respondents in the study were Chinese. The academic disciplines of the participants are listed in Table 1.
| Faculty | n (%) |
|---|---|
| Architecture | 30 (5.4%) |
| Arts | 70 (12.5%) |
| Business & Economics | 96 (17.1%) |
| Dentistry | 10 (1.8%) |
| Education | 31 (5.5%) |
| Engineering | 64 (11.4%) |
| Law | 25 (4.5%) |
| Medicine | 88 (15.7%) |
| Science | 92 (16.4%) |
| Social Sciences | 52 (9.3%) |
| Missing | 2 (0.4%) |
| Total | 560 (100%) |
Procedures
The study adopted a prospective longitudinal design. Participants were invited to fill in a paper survey during the registration period, which included measures of EI and self‐directed learning, as well as items on the background information of students (e.g. gender, age and faculty). To prevent self‐presentation, students were told that the aims of the survey were to better understand their learning attitudes and personal emotional experiences, instead of measuring their EI and self‐directedness in learning. Then, they were followed up via email to answer an online survey at the end of the first year, so as to tap students’ GPA and their satisfaction with the university experience, as well as their achievement of the various generic outcomes. Participation in the study was entirely voluntary.
Measures
Emotional intelligence
EI was measured via the 33‐item Emotional Intelligence Scale (EIS) (Schutte et al., 1998), developed on the basis of Salovey and Mayer's (1990) model of EI. The scale has six key dimensions, including: (i) appraisal of emotions in the self (AES) (e.g. ‘I am aware of my emotions as I experience them’); (ii) appraisal of emotions in others (AEO) (e.g. ‘I know what other people are feeling just by looking at them’); (iii) emotional regulation of the self (ERS) (e.g. ‘I seek out activities that make me happy’); (iv) emotional expression (EE) (e.g. ‘I know when to speak about my personal problems to others’); (v) emotional regulation of others (ERO) (e.g. ‘I help other people feel better when they are down’); and (vi) utilisation of emotions in problem solving (UEPS) (e.g. ‘I use good moods to help myself keep trying in the face of obstacles’) (Zhoc et al., 2017). The internal consistency of the scale was reported by Zhoc et al. (2017) to range from 0.85 to 0.93.
Self‐directed learning
Self‐directed learning was measured using the Self‐Directed Learning Scale (SDLS). The SDLS consists of 10 items, and is a uni‐dimensional scale developed by Lounsbury and Gibson (2006). Respondents have to indicate their level of agreement on a five‐point Likert scale from 1 (strongly disagree) to 5 (strongly agree). A higher total score on the SDLS would imply a higher level of self‐directedness in learning. Examples of the items include: ‘If there is something I don't understand in a class, I always find a way to learn it on my own’, ‘I am very motivated to learn on my own without having to rely on other people’, and so on. The internal consistency of the scale was reported to range from 0.84 to 0.87 in the college samples (Lounsbury et al., 2009).
Student learning outcomes
In the follow‐up survey, there was a 15‐item Student Learning Outcomes Scale (SLOS) that was designed to tap students’ self‐evaluation on the achievement of the generic learning outcomes. The scale was devised with reference to the educational aims and institutional learning outcomes for the undergraduate programme of the sampled university, which could be categorised broadly into the cognitive, social and self‐growth outcomes of students. The details of items in each category are listed in Table 2. The confirmatory factor analyses (CFA) results supported the three‐factor model of the scale (see Table 4 later for more details). Besides, the Cronbach alpha coefficients for the social, cognitive and self‐growth outcomes in this study were 0.87, 0.83 and 0.83, respectively, indicating that all three factors were internally consistent. Apart from the above, there were two items that gauged students’ end‐of‐year GPA and their satisfaction with the university experience (‘overall evaluation of the first‐year university experience’).
| Cognitive outcomes | Social outcomes | Self‐growth outcomes |
|---|---|---|
| 1. Dealing with unfamiliar problems | 1. Communicating effectively with others | 1. Managing time more effectively |
| 2. Thinking creatively | 2. Greater understanding of others | 2. Learning a new skill or knowledge by yourself |
| 3. Thinking analytically and critically | 3. Getting along with people of different cultural and ethnic backgrounds | 3. Ability to have critical self‐reflection |
| 4. Viewing things from a global perspective | 4. Working collaboratively with others | 4. Lifelong learning |
| 5. Developing in‐depth knowledge in my areas of study | 5. Leadership skills | 5. Upholding personal and professional ethics |
Data‐analytic plan
Structural equation modelling (SEM) (AMOS 23.0) was performed to conduct measurement and structural analyses. Prior to testing the structural model, CFA were performed to validate the factor structure of the three measurement scales used in the study. In addition to the conventional χ2 test that was used to evaluate whether the data deviate from the model significantly, a number of fit statistics were also employed to assess the model fit [e.g. comparative fit index (CFI), goodness‐of‐fit index (GFI), normed fit index (NFI), non‐normed fit index (NNFI) and root mean square error of approximation (RMSEA)]. According to Hu and Bentler (1999), values of CFI, NFI and NNFI should be over 0.95 so as to be recognised as a good fit, although 0.90 is considered by many researchers as an acceptable cutoff (Bentler & Bonett, 1980). As to the RMSEA, a value of 0.05 or less indicates a close model fit (Browne & Cudeck, 1993), while a value below 0.08 is still considered acceptable (MacCallum et al., 1996).
The purpose of performing SEM was to verify our hypothesised model that illustrates the structural relationships among EI, self‐directed learning and the various student learning outcomes as a whole, including students’ GPA, generic outcomes and their satisfaction with the university experience. Since EI is a latent factor in the model, multiple regression analyses were employed to further tease out whether and how the six factors of EI are significant in influencing self‐directed learning and the achievement of generic outcomes.
Results
Preliminary analyses: Correlations among study variables
As shown in Table 3, EI was significantly correlated with self‐directed learning (r = 0.46, p < 0.01). Moreover, except for GPA, EI was positively associated with students’ satisfaction with their university experience (r = 0.13, p < 0.01) and their attainment of generic learning outcomes (r = 0.31, p < 0.01). As to self‐directed learning, it was positively correlated with GPA (r = 0.12, p < 0.01) and generic learning outcomes (r = 0.26, p < 0.01), as well as students’ satisfaction with their university experience (r = 0.14, p < 0.01). It is worth noting that GPA (r = 0.25, p < 0.01) and students’ attainment of generic leaning outcomes (r = 0.51, p < 0.01) were both positively associated with students’ satisfaction with their university experience.
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | |
|---|---|---|---|---|---|---|---|---|
|
– | |||||||
|
0.46a | – | ||||||
|
0.02 | 0.12a | – | |||||
|
0.13a | 0.14a | 0.25a | – | ||||
|
0.31a | 0.26a | 0.15a | 0.51a | – | |||
|
0.28a | 0.25a | 0.15a | 0.53a | 0.92a | – | ||
|
0.30a | 0.21a | 0.08 | 0.46a | 0.87a | 0.73a | – | |
|
0.27a | 0.24a | 0.18a | 0.48a | 0.89a | 0.77a | 0.66a | – |
Notes
- a p < 0.01.
- UES = satisfaction with the university experience.
Measurement models
Confirmatory factor analyses were performed to test the measurement models of the EIS, SDLS and SLOS. All the fit statistics indicated a good fit of the data to the measurement models tested (see Table 4).
Structural model: Relationships among EI, self‐directed learning and student learning outcomes
The results of the SEM supported that the model was tenable [χ2(174) = 348.03, p < 0.001, GFI = 0.94, CFI = 0.95, RMSEA = 0.04, NFI = 0.91, NNFI = 0.94] (see Figure 1). As expected, EI had quite a substantial association with self‐directed learning (β = 0.62, p < 0.005) and was able to explain a total of 38% of its variance. Self‐directed learning, in turn, was positively related to both the GPA (β = 0.15, p < 0.005) and generic learning outcomes (β = 0.14, p < 0.005). While EI was positively associated with generic learning outcomes (β = 0.20, p < 0.005), it was found to have non‐significant association with GPA. Furthermore, the results showed that both the GPA (β = 0.17, p < 0.005) and generic learning outcomes (β = 0.49, p < 0.005) were positively linked with students’ satisfaction with their university experience.
Model illustrating the relationships among EI, self‐directed learning and different student learning outcomes
Note: AEO = appraisal of emotions in others; AES = appraisal of emotions in the self; CogOut = cognitive outcomes; EE = emotional expression; ERO = emotional regulation of others; ERS = emotional regulation of the self; Generic LOs = generic learning outcomes; SDL = self‐directed learning; SGOut = self‐growth outcomes; SocOut = social outcomes; UEPS = utilisation of emotions in problem solving; UES = satisfaction with the university experience. [Colour figure can be viewed at wileyonlinelibrary.com]
Overall, the model was able to explain 5% of the variance in GPA, 9% of the variance in generic learning outcomes and 30% of the variance in students’ satisfaction with their university experience, respectively.
Direct, indirect and total effects of EI and self‐directed learning on student learning outcomes
To better delineate the relationships among the variables, the direct, indirect and total effects of EI and self‐directed learning on the various student learning outcomes are presented in Table 5. It is worth noting that EI was found to have a significant total effect on both the generic learning outcomes (β = 0.29, p < 0.005) and students’ satisfaction with the university experience (β = 0.14, p < 0.005), but not on the GPA. Having said that, based on 1,000 bootstrap samples, we found that through self‐directed learning, EI had significant indirect effects on students’ GPA (β = 0.14, p < 0.05), generic learning outcomes (β = 0.09, p < 0.05) and satisfaction with the university experience (β = 0.14, p < 0.005).
| GPA | Generic learning outcomes | Satisfaction with the university experience | |||||||
|---|---|---|---|---|---|---|---|---|---|
| Direct | Indirect | Total | Direct | Indirect | Total | Direct | Indirect | Total | |
| EI | −0.13 | 0.14* | 0.01 | 0.20* | 0.09* | 0.29** | – | 0.14** | 0.14** |
| SDL | 0.15* | 0.03* | 0.18* | 0.14** | ‐ | 0.14** | – | 0.10* | 0.10* |
Notes
- **p < 0.005; *p < 0.05.
Multiple regression analyses examining how the different emotional abilities of EI influence students’ self‐directed learning
The multiple regression analyses (see Table 6) clearly delineate that the three emotional abilities of EI (i.e. ERS, ERO and AES) were able to explain 26% of the variance in self‐directed learning [adjusted R2 = 0.26, F(3, 556) = 65.55, p < 0.001]. Among the three emotional abilities of EI, ERS was the most influential (β = 0.29, p < 0.001), explaining the largest proportion of the variance in self‐directed learning (adjusted R2 = 0.21). This was followed by ERO (β = 0.21, p < 0.001) and AES (β = 0.13, p < 0.005). As to generic learning outcomes, the emotional abilities of UEPS (β = 0.26, p < 0.001) and ERO (β = 0.21, p < 0.001) were able to explain a total of 10% of the variance [adjusted R2 = 0.10, F(2, 557) = 31.54, p < 0.001].
| Standardised β | p | Adjusted R2 change | Adjusted R2 | F | p | |
|---|---|---|---|---|---|---|
| Self‐directed learning | ||||||
| ERS | 0.29 | 0.00a | 0.21 | |||
| ERO | 0.21 | 0.00a | 0.04 | 0.26 | 65.55 | 0.00a |
| AES | 0.13 | 0.00a | 0.01 | |||
| Generic learning outcomes | ||||||
| UEPS | 0.21 | 0.00a | 0.08 | 0.10 | 31.54 | 0.00a |
| ERO | 0.17 | 0.00a | 0.02 | |||
Notes
- a p < 0.005.
Discussion
The aim of the present study was to examine the effects of EI on self‐directed learning and how EI and self‐directed learning contribute to key learning outcomes in higher education, including academic achievement, generic learning outcomes (i.e., social, cognitive and self‐growth outcomes) and students’ satisfaction with their university experience. There are several findings that are worth noting.
Firstly, the study expands the extant literature by affirming the role of EI on self‐directed learning, which in turn is shown to enhance both the GPA and generic learning outcomes, including cognitive, social and self‐growth outcomes. Apart from concurring with the results of previous studies (Buvoltz et al., 2008; Muller, 2008) in showing the significant association between EI and self‐directed learning, the present study, as a step further, finds that there are three emotional abilities that are significant in driving the occurrence of self‐directed learning: ERS, AES and ERO.
As expected, emotional regulation of the self is the most crucial for self‐directed learning. Given that self‐directed learners are goal oriented, it is critical for them to be self‐disciplined so as to realise their self‐defined learning goals (Kirwan et al., 2014). Indeed, in the process of achieving learning goals, self‐regulation of emotions is important so as to override different temptations and short‐term attractions that may lead to distraction and procrastination in goal‐directed behaviour. Apart from this, as mentioned before, the process of self‐directed learning is painful as students have to experience negativity, confusion, frustration and dissatisfaction with their learning experience when they enter a new learning experience. The ability to regulate negative emotions is therefore critical, to sustain study effort and persist in the face of setbacks in the course of pursuing goals.
As to the appraisal of emotions in the self, it is an important process facilitating self‐reflection, which in turn is critical for self‐directed learning. Given that emotions are signals providing information to an individual on what is valuable and meaningful, an individual who is well aware of their own emotions can make good use of this emotional information to guide thinking and action. For example, in the process of goal setting for self‐directed learning, the desirability of the goal is a function of the positive and negative emotional consequences of goal pursuit and achievement or failure in achieving the goal. Similarly, in the process of monitoring and evaluating progress during self‐directed learning, individuals will use feedback from their emotions to determine actions for further self‐regulation (or not) in achieving the intended goals (Salovey, 1992). That said, the appraisal of emotions in the self is critical for self‐directed learning.
Meanwhile, for the emotional regulation of others, it is facilitative of intellectual exchanges with others in the highly interactive learning environment in higher education. Although self‐directed learners are self‐reliant in learning, intellectual exchanges with teachers and peers are critical parts of the learning process that not only nurture cognitive growth and intrinsic motivation, helping individuals to delve into the subject matter, but may also inspire a solution to the problems encountered during learning. In short, self‐directed learning is fostered by helpful interactions and relationships with other people, who can offer advice and insight that facilitate individuals to progress in the course of achieving the learning goals. This explains why emotional regulation of others is significant for self‐directed learning.
Secondly, the study provides more evidence on the relationship between self‐directed learning and student learning outcomes. Specifically, it shows that self‐directed learning positively affects not only the GPA, but also students’ development of generic outcomes, including social, cognitive and self‐growth outcomes. In other words, individuals with a higher level of self‐directedness tend to gain more in both academic and non‐academic terms. This is understandable considering that, in the process of learning, self‐directed learners tend to take the initiative and are self‐regulated in learning, which leads them to exhibit behaviours (such as taking a deep learning approach, setting higher learning goals and having more discussion and collaboration with faculty and peers) that help them to achieve more in both academic and non‐academic terms. It is worth noting that both the GPA and the generic learning outcomes are influential on students’ satisfaction with the university experience, with the latter having a much greater weight than the former, indicating that university students’ focus is no longer on academic performance only, but on the extent of achieving personal development such as leadership skills, problem‐solving and communication skills, as well as analytical and creative thinking, and so on. In other words, the findings in this study correspond to those of Lounsbury et al. (2009), in which self‐directed learners were found to be more optimistic, conscientious, self‐actualised and open to new experiences. They not only achieved higher academic performance, but also had higher college satisfaction.
Thirdly, EI is found to pose a significant direct impact on the generic student learning outcomes. Further analyses show that UEPS and ERO are influential in students’ achievement of generic outcomes, which are largely acquired through the processes of interacting with peers and teachers as well as participating in educational out‐of‐class activities (such as community work, internship and serving in student organisations). For UEPS and ERO, they are both significant in social interactions. For example, the expression of positive emotions, the suppression of negative communication with others, the control of emotional responses to private problems are all important in enhancing interpersonal relationships (Salovey & Mayer, 1990). The achievement of the generic learning outcomes, in turn, can facilitate academic performance. After all, academic learning involves a lot of cognitive activities, which requires having different cognitive skills, such as problem‐solving skills and viewing things from a global perspective. Besides, social skills may also facilitate academic performance through quality interaction with teaching staff and peers, as cognitive abilities can be enhanced through active discussion of ideas, debating points of view and critically reviewing work with peers or teachers (Guthrie & Wigfield, 2000).
Last but not least, while it was found in many studies that there was a direct relationship between EI and academic performance, this study shows that the relationship between the two is mediated by students’ self‐directedness in learning. Moreover, EI may differentially predict success by major subject area, with social and emotional skills more crucial for liberal arts than for sciences (Parker et al., 2009). Indeed, there were concurring results reported by Zhoc et al. (2017), who found that EI was positively associated with students’ GPA in the faculties of business and social sciences only, not with all students as a whole. Since subjects in both faculties are people oriented, it is likely that a good knowledge and understanding of the emotions of the self and others could contribute to better academic performance in studies which are related to an understanding of human behaviour (e.g. business, psychology, sociology and political science).
The study is significant as it not only establishes the affirmative relationship between EI and self‐directed learning, but also teases out the specific emotional abilities that are influential on self‐directed learning and generic learning outcomes. Given that EI is shown to be modifiable through training or intervention programmes (Di Fabio & Kenny, 2011; Vesely et al., 2014), a practical implication of this study is that relevant interventions may be incorporated in the university orientation programme so as to lay a better psychological foundation that fosters students’ optimal functioning in the university. In addition, an inclusion of EI as part of the standard college curriculum could lead to many potential personal, social and societal benefits (Vandervoort, 2006).
Despite the positive findings, there are several limitations in this study that should be noted. Firstly, the study sample was restricted to one university in Hong Kong. Since the sample was not representative of higher education as a whole, the study should be replicated in other higher institutions so as to ensure the generalisability of the results. Besides, the study made use of self‐reported GPA. Although there is a high correlation between self‐reported and actual college GPA (r = 0.90), the use of actual academic achievement from school records could further ensure the reliability of the data (Kuncel et al., 2005). Moreover, self‐report measures are used to assess EI, self‐directed learning and the generic learning outcomes, which may be subject to response biases and cannot accurately reflect actual behaviours (Mega et al., 2014). Behavioural measures may be needed as well in future studies.
To conclude, students who are more emotionally intelligent have higher self‐directedness, which leads them to achieve more not only in academic terms, but also in personal development including social, cognitive and self‐growth aspects. As a result, they are also more satisfied with their overall university experience. To facilitate self‐directed learning, there are three crucial emotional abilities of students that should be strengthened: ERS, AES and ERO.
