Content uploaded by George Saadé
Author content
All content in this area was uploaded by George Saadé on Sep 17, 2018
Content may be subject to copyright.
M. S. Khine & S. Areepattamannil (Eds.), Non-cognitive Skills and Factors in Educational
Attainment, 65–85.
© 2016 Sense Publishers. All rights reserved.
MARIA-JOSE SANCHEZ-RUIZ, JAMIL EL KHOURY,
GEORGE SAADÉ AND MIRIAM SALKHANIAN
4. NON-COGNITIVE VARIABLES AND
ACADEMIC ACHIEVEMENT
The Role of General and Academic Self-Efficacy and
Trait Emotional Intelligence
INTRODUCTION
Mapping the individual differences that predict academic success in higher education
is key within educational and vocational settings because academic performance
(AP) is an indicator of prospective success and accomplishments and opens the
door for job opportunities (Strenze, 2007). In educational settings, acknowledging
and assessing these differences and the roles they play on academic success can
be useful (e.g., when developing personalized interventions to increase academic
achievement).
Early research on the predictors of AP found that intelligence (as measured by IQ
and aptitude tests), as well as previous achievement (as measured by GPA), were the
strongest predictors of AP (Sinha, 1966; Touron, 1987; Rohde & Thompson, 2007;
Kuncell & Hezlett, 2010). However, several lines of inquiry have suggested that, in
order to attain accuracy in predicting academic achievement, a heuristic approach
needs to be adopted. Empirical evidence shows that non-intellective variables such
as personality traits, emotion, and motivation, may directly or indirectly predict
university AP (e.g., Robbins, Lauver, Le, Davis, Langley, & Carlstrom, 2004). Some
of those non-cognitive factors seem to predict AP over and above intelligence (e.g.,
Sanchez-Ruiz, Mavroveli, & Poullis, 2013).
The present chapter focuses on some of the non-cognitive factors mentioned
above that have shown to influence in academic achievement. After reviewing
the empirical evidence on the role of traditional personality traits and academic
motivation, we turn our focus on self-efficacy constructs, in particular academic and
emotional self-efficacy, as they relate to academic achievement.
PERSONALITY TRAITS AND AP
One of the leading psychological factors that influence AP is personality (e.g.,
Richardson et al., 2012). Some studies have reported that personality traits show
incremental validity over other variables such as cognitive ability and gender in
M.-J. SANCHEZ-RUIZ ET AL.
66
the prediction of AP (e.g., Furnham, Chamorro-Premuzic, & McDougall, 2002;
Richardson et al., 2012). Researchers have primarily focused on traditional personality
hierarchies, namely the Five Factor Model (FFM; McCrae & Costa, 1997).
Among the five personality dimensions, conscientiousness has been the one
most consistently related to AP (see Poropat, 2009, for a review) across samples
and measures (Noftle & Robins, 2007). Findings from studies (e.g., Furnham et al.,
2002) and meta-analyses (Richardson et al., 2012; Trapmann, Hell, Hirn, & Schuler,
2007) indicate that university AP correlates positively with conscientiousness.
Furthermore, a study among undergraduates suggests that conscientiousness is the
one personality trait that predicts AP consistently across the three academic years of
the university degree (Sanchez-Ruiz, Pérez-González, Fayad, Filella, & Soldevila,
in progress).
An explanation for these findings lies in the association between conscientiousness
and effortful strategies that are beneficial to learning in educational settings, which
in turn promote AP (e.g., Corker, Oswald, & Donnellan, 2012). This is in line with
research indicating that being motivated to succeed, organized and disciplined,
has a beneficial impact on study habits and increases academic commitment
(Komarraju et al., 2011; Poropat, 2009). In addition, the abovementioned findings
can be accounted for by the relationship between conscientiousness and higher-
order thinking skills, such as executive function, working memory capacity, and
other neurobiological underpinnings of the prefrontal cortex (e.g., DeYoung et al.,
2010). In addition, a recent study found that trait conscientiousness acts as a catalyst
by enhancing the relation between intelligence and AP (Di Domenico & Fournier,
2015).
Openness to experience has also been linked to AP (e.g., Komarraju et al., 2011;
Propat, 2009) and has shown to be a strong predictor of SAT verbal scores (Noftle &
Robins, 2007). This trait, also referred to as “intellect”, affords intellectual curiosity,
which is a drive for learning and can have a positive impact on academic success. In
this regard, Chamorro-Premuzic and Furnham (2008) reported that students scoring
high on openness have a rich vocabulary repertoire, are open to novel ideas, and
think in an abstract way, all of which support the positive relation between openness
and AP. Conversely, other studies have found that openness and AP are negatively
correlated, possibly due to the difficulty in following rules and meeting deadlines
among high-openness scorers (Chamorro-Premuzic & Furnham, 2004; Kappe &
van der Flier, 2010).
Individuals high in neuroticism (low on emotional stability) tend to be more
anxious, tense, vulnerable, and focus mainly on their emotional state (Costa &
McCrae, 1992). Students high on neuroticism are susceptible to higher level of stress
under academic demands, such as exam performance, and distraction from their
academic work, both of which can lead to poorer performance. This explanation
finds support in longitudinal studies reporting a negative correlation between
neuroticism, exam performance, and final-project grades (Chamorro-Premuzic &
NON-COGNITIVE VARIABLES AND ACADEMIC ACHIEVEMENT
67
Furnham, 2003). These findings are in line with those of other studies (e.g.,
Furnham & Monsen, 2009; Lounsbury, Sundstrom, Loveland, & Gibson, 2003) and
meta-analyses (e.g., Trapmann et al., 2007).
There are mixed findings regarding the role of extraversion in AP. Several studies
have reported a positive relation between the two constructs (e.g., Chamorro-Premuzic
& Furnham, 2003; Komarraju et al., 2011). A tentative explanation for this is that
extraverts might have ample energy to help them endure more academic mundane tasks
than their introverted counterparts. Also, extraverts might be benefitting from social
support, teamwork and networking in their academic endeavors to a greater extent than
introverts might.
The relationship between AP and extraversion could be moderated by the type
of assessment used. For example, Furnham et al. (2004) reported extraversion to
be positively correlated with final-project but not with exam grades, suggesting
that the social skills used in the interaction with the supervisor could play a role.
Other studies have found a negative relationship between extraversion and AP
(e.g., Furnham, Nuygards, & Chamorro-Premuzic, 2013), which might be due to
extraverts diverging from academic tasks and orienting more towards socializing,
thus allocating little time and energy for studying.
The personality trait resulting in the most mixed results when explored in
relation with AP is agreeableness. Some findings indicate a positive relationship
between agreeableness and classroom behavior (Furnham, Chamorro-Premuzic, &
McDougall, 2002), but not necessarily with AP. However, meta-analyses indicate a
small correlation between agreeableness and AP (e.g., Poropat, 2009).
However, extraversion, openness to experience, and agreeableness have been non-
significant predictors of AP in a few studies (e.g., Poropat, 2009), which contradicts
previous findings. Also, in Komarajju et al. (2011), there was no significant
relation between extraversion and AP and there was a positive relation between
neuroticism an AP. A possible reason for the discrepancies regarding the relation
between some personality traits and AP could be the potential extraneous effect
of the academic major. There are numerous studies demonstrating that personality
and emotion-related traits of university students vary across academic majors
(e.g., Sanchez-Ruiz, Pérez-González, & Petrides, 2010), but fewer studies focus on
the differential relationship between personality traits and AP by major. One such
study by Vedel, Thomsen, and Larsen (2015) found that conscientiousness, followed
by openness, positively predicted AP. Extraversion negatively predicted AP among
psychology students only, and openness positively predicted AP among political
science students only. These findings suggest that certain traits might be important
for the academic success in certain disciplines and future studies would benefit from
incorporating academic major into their designs.
It is worth noting that all of the above reviewed studies used self-rated personality
measures (e.g., NEO-Personality Inventory Revised, Costa, & McCrae, 1992), and
so scores can be influenced by social desirability. However, a recent meta-analysis
M.-J. SANCHEZ-RUIZ ET AL.
68
explored the relationship between personality traits rated by close individuals such
as friends or family members (referred to as other-rated as opposed to self-rated
traits), and AP, with results indicating that this association has a similar direction,
yet stronger, than that between self-rated personality traits and AP (Poropat, 2014).
Not surprisingly, in the same meta-analysis, conscientiousness was the strongest
correlate of AP followed by openness (moderate correlation). The rest of the
personality traits showed weak correlations with AP. Furthermore, other-rated
personality traits collectively had an incremental predictive validity on AP over and
above intelligence. However, while controlling for intelligence, openness had the
strongest correlation (Poropat, 2014).
In sum, conscientiousness and openness to experience have been commonly
associated with AP, followed by extraversion and neuroticism (e.g., Chamorro-
Premuzic & Arteche, 2008). While conscientiousness has been a consistent correlate
of AP throughout a wealth of studies, there are mixed findings regarding the other
four traits.
ACADEMIC MOTIVATION
Old and new findings suggest that academic motivation is a prominent non-cognitive
contributor to AP (Amrai, Motlagh, Zalani, & Parhon, 2011; Daoust, Vallerand, &
Blais, 1988; Vecchione, Alessandri, & Marsicano, 2014), even beyond cognitive
ability (Spinath, Spinath, Harlaar, & Plomin, 2006). The construct of academic
motivation is grounded in the self-determination theory (SDT: Deci, Vallerand,
Pelletier, & Ryan, 1991), which distinguishes the various drives toward task
engagement, and suggests that individuals have an innate tendency to express their
interests, activate and develop their potentials, and overcome challenges.
According to the SDT, motivation is a continuous quality rather than a static
trait. This continuum ranges from intrinsic motivation, at one end, to amotivation,
at the other end. In between these two poles lies extrinsic motivation, which is
also considered a continuum ranging from integrated regulation (closer to intrinsic
motivation), identified regulation, introjected regulation, and external regulation
(closer to amotivation). Self-determination can mainly be achieved through exercising
intrinsic motivation, which is engaging in an activity driven by the genuine interest
in it rather than by external forces or rewards (e.g., extrinsic motivation), is the
stepping stone to reaching high self-determination.
The self-determination theory identifies three basic psychological needs: (1)
Competence, which refers to the need to gain positive feedback on performance and
for perceived capability to master a task; (2) Autonomy, which refers to the need of
one’s course of action to be driven by one’s own initiative and interest, and a need
to be self-regulated; (3) Relatedness, which refers to the need for close relations and
interaction with other people. There is evidence that the fulfillment of these basic
needs in students promotes self-regulation for learning, AP, and ultimately, well-
being (Niemiec & Ryan, 2009).
NON-COGNITIVE VARIABLES AND ACADEMIC ACHIEVEMENT
69
This approach has numerous applications in educational settings. Intrinsic
motivation is seen as conducive to learning and performance. It is sustained by the
satisfaction of two of the basic needs mentioned above (Competence and Autonomy;
Niemiec & Ryan, 2009). For example, students may feel competent when they
have a sense of ability to meet the challenges of academic work; and autonomous
when they study willingly and not out of obligation, which might contribute to
better performance. Thus, self-determination is a result of interest in and valuing of
education, which are, in turn, predictive of AP (Deci et al., 1991).
On the one hand, some types of extrinsic motivation can hinder AP. External
regulation (which is closest to amotivation) favors behaviors that are aiming solely
at obtaining a reward (e.g., grades, or praise) or to avoid a punishment (e.g., failing,
being ridiculed). Once these conditions are removed, the motivation diminishes,
which might actually hinder AP. In introjected regulation, behaviors are performed
in order to fulfill internal contingencies, such as self-aggrandizement. For example,
a student with this kind of motivation might study to feel pride or to avoid guilt-
feeling.
On the other hand, some types of extrinsic motivation can facilitate AP. Identified
regulation and integrated regulation are at the most autonomous end of the spectrum,
closest to intrinsic motivation. Identified regulation refers to motivation to perform
behaviors because of their significance and value. In this case, students may study
a subject because it is important for their future career. In integrated regulation,
identified regulations are combined with other aspects of the self. For example,
students may be motivated to study psychology as doing so will enable them to
help others in need, which might be in accord to their personal values, interests, and
traits, such as empathy. However, these influences of different types of extrinsic
motivation on AP will need to be further explored in future studies as they remain
under-researched.
The self-determination approach is well-supported by research, and in particular,
the impact of intrinsic motivation. For example, a recent 40-year meta-analysis
indicates that intrinsic motivation is a moderate-strong predictor of performance
in educational and work domains (Cerasoli, Nicklin, & Ford, 2014). Additionally,
intrinsic motivation positively influences the learning process and the quality of
learning, while lack of motivation has been related to poor psychosocial adjustment
to university life (Baker, 2004), which can, in turn, hinder AP.
SELF-EFFICACY
Another aspect of personality that is widely studied in educational psychology is
perceived self-efficacy, derived from the social-cognitive theory (Bandura, 1977).
Self-efficacy is closely linked to the competence domain of the SDT theory, and
has been conceptualized as a compilation of self-perceptions of capabilities, skills,
and competencies which function in different domains, and exert control over one’s
own environment and level of functioning (Bandura, 1977). The construct has been
M.-J. SANCHEZ-RUIZ ET AL.
70
applied in different domains of functioning including academic, emotional, and
social, and is commonly measured by self-report scales. According to the theoretical
framework of self-efficacy, expectancies of personal efficacy determine coping
behavior, optimism or pessimism, extent of efforts exerted, and perseverance in
the face of obstacles and adversities (Bandura, 1995). Self-efficacy has empirically
demonstrated to influence a person’s level of motivation, perseverance, adaptation,
subjective well-being, and vulnerability to depression and stress (Bandura, 1997;
Strobel, Tumasjan, & Spörrle, 2011).
Academic Self-Efficacy
For decades, research on perceived self-efficacy has been widely applied in
educational settings. It is evidenced from early research that students who score
high on self-efficacy work harder, participate and persevere more, and have less
negative responses to stressors than their low self-efficacy counterparts (Bandura,
1997; Zimmerman, 2000). One of the most commonly used self-efficacy construct
in educational settings is academic self-efficacy, which is defined as self-perceptions
of capabilities to manage academic work and achieve, and there is solid evidence
that it predicts academic outcomes (Bandura, Barbaranelli, Caprara, & Pastorelli,
1996). Studies and meta-analyses indicate a well-established positive relationship
between academic self-efficacy and AP, over and above other predictors, such as
cognitive ability and high school AP (Komarraju & Nadler, 2013; Lee, Lee, & Bong,
2014; Multon, Brown, & Lent, 1991; Richardson et al., 2012). Additionally, Khan
(2013) studied the association between academic self-efficacy, coping strategies
and AP. In particular, academic self-efficacy positively correlated with positive
reinterpretation, growth, acceptance, and planning, all of which upsurge AP, and
negatively with maladaptive strategies to cope with stress (e.g., substance abuse). In
addition, Chemers, Hu, and Garcia (2001) longitudinally explored the relationship
between academic self-efficacy and AP, commitment to remain in university,
academic expectations, and perceived coping abilities in university students. Results
indicate that academic self-efficacy has both direct and indirect effects on AP.
The Academic Self-efficacy Scale (ASE: Mcllroy et al., 2000) is one of the most
commonly used measures of self-efficacy in educational settings. The ASE has been
used in several studies exploring the relation between academic self-efficacy and AP,
while showing a strong reliability score of 0.83 (Lawler, 2012).
Emotional Self-Efficacy (Trait Emotional Intelligence)
Emotion-related personality traits, such as psychological well-being, have been
found to contribute to AP (e.g., Vaez & Laflamme, 2008), through the willingness to
exert effort towards accomplishing academic tasks and the positive affect component
(Ayyash-Abdo & Sanchez-Ruiz, 2012). However few research has systematically
NON-COGNITIVE VARIABLES AND ACADEMIC ACHIEVEMENT
71
studied the role of emotion-related traits, not covered by existing personality
trait taxonomies, in AP. One exception is trait emotional intelligence (trait EI or
emotional self-efficacy; Petrides, 2011), which is conceptualized as a constellation of
emotion-related self-perceptions located at the lower level of hierarchical personality
structures, and assessed through typical-performance instruments (Petrides, 2011).
Trait EI can also be understood as a collection of affective dispositions linked to
well-being that are useful in social interactions and thus considered adaptive (Pérez-
González & Sánchez-Ruiz, 2014).
Trait EI is to be distinguished from ability EI. One of the most important
distinctions between the two constructs is that trait EI provides a more comprehensive
operationalization of the affect-related aspects of personality than traditional
personality models (Pérez-González & Sanchez-Ruiz, 2014) and lies wholly outside
the taxonomy of human cognitive ability (Carroll, 1993). In contrast, ability EI seeks
to measure emotionality through maximum performance tests (Petrides, Furnham, &
Mavroveli, 2007), which has shown to be problematic because of the subjective
nature of emotion (Brody, 2004).
The construct of trait EI originated and developed within the field of individual
differences in emotionality (e.g., Matthews, Deary, & Whiteman, 2009), while
ability EI belongs to the cognitive dimension. Thus, the two constructs represent
two different lines of research and distinct operationalizations (evidence of this can
be found in the low correlations reported between the two—Petrides, Furnham, &
Mavroveli, 2007). For more information on the ability vs. trait conceptualizations of
trait EI, please see Petrides, 2011.
Trait EI plays a role in various variables in educational contexts, especially AP. The
advantageous effect of trait EI has been shown in a recent meta-analysis (Perera &
DiGiacomo, 2013), suggesting that the construct’s influences AP moderately, and
its effect depends on sample characteristics (see Mavroveli & Sanchez-Ruiz, 2011
for a comprehensive review). However, several studies have explored the relation
between trait EI and AP among university students, reporting a significant association
(Parker et al., 2004). In addition, trait EI has shown incremental validity over and
above cognitive abilities and the Big Five personality traits in higher education (e.g.,
Sanchez-Ruiz et al., 2013).
Some research, however, has found weak or null correlations between trait EI
and academic success (e.g., Newsome & Day, 2000). Some inconsistent findings
regarding the relationship between trait EI and AP might be due to such relationship
being different across academic domains. In fact, trait EI differs across domains
(Sanchez-Ruiz, Perez-Gonzalez, & Petrides, 2010) and appears to be more important
for academic achievement in social sciences than in other disciplines (Sanchez-
Ruiz et al., 2013). Thus, more research looking into different domains needs to
be conducted to further elucidate the mechanisms by which trait EI operates in
particular academic contexts, such as medical education (Austin et al., 2005; Chatila
et al., in progress; Fallahzadeh, 2011), whereby trait EI might have an impact in the
M.-J. SANCHEZ-RUIZ ET AL.
72
patient-doctor relationship. Another tentative explanation for the low correlations
found is that often, indirect effects seem to be more important than direct ones in a
number of studies (Perera & DiGiacomo, 2015; see following section).
Trait EI has been linked to academic variables other than AP. High trait EI
university students also score higher on certain measures of creative skills (Sanchez-
Ruiz, Hernández-Torrano, Pérez-González, Batey, & Petrides, 2011), which are
crucial for academic and work success. Regarding primary and secondary education,
absenteeism, for example, has been less reported among high trait EI students than
their low trait EI counterparts, and the same is true for the number of expulsions from
school due to misconduct (Mavroveli, Petrides, Shove, & Whitehead, 2008). Trait
EI can have a positive impact on children’s peer relations at school and decrease
the likelihood of disruptive and violent behavior (Santesso, Reker, Schmidt, &
Segalowitz, 2006) as well as bullying (Mavroveli & Sanchez-Ruiz, 2011).
There have been some criticisms regarding certain trait EI assessment tools (see
Pérez-González, Petrides, & Furnham, 2005 for a review), due to lack of robustness
of their psychometric properties or because they claim to measure ability EI when
they are really assessing trait EI through self-report. One of the most reliable, valid,
and widely used tools to measure trait EI is the TEIQue which has shown excellent
psychometric properties across samples (e.g., Freudenthaler, Neubauer, Gabler,
Scherl, & Rindermann, 2008; Mikolajczak, Luminet et al., 2007; Petrides, Pérez-
González et al., 2007). This questionnaire is the result of a systematic analysis of
previous models of EI and covers 15 facets encompassed in four factors, namely
Well-being, Self-control, Emotionality and Sociability. There are a wide variety of
versions of the test (e.g., Short form, Child form, Adolescent form, etc.) and it has
been translated into more than 15 languages.
INDIRECT EFFECTS
Due to the complexity and interconnected network of the effects of various cognitive
and non-cognitive determinants of AP, oftentimes the relationships between the
aforementioned constructs and AP is not a direct one. To delineate the mechanisms
operating in such network, indirect effects need to be considered. As such, research
has tried to test models of direct and indirect effects, normally through structural
equational modeling (SEM) or path analysis in order to understand how specific
factors mediate the relation between another non-cognitive factor and AP.
Indirect Effects of Personality and Academic Motivation on AP
Conscientiousness has shown to have an indirect effect on AP via learning approaches,
such as learning strategies (Diseth, 2013). The mediation of students’ learning
approaches between conscientiousness and AP is not surprising since students
who engage in a strategic learning approach effectively require the organization
of their work in accordance with their academic demands. In addition, openness
NON-COGNITIVE VARIABLES AND ACADEMIC ACHIEVEMENT
73
to experience indirectly promotes AP through other learning strategies, such as
deep elaborative processing and synthesis analysis (Komarraju et al., 2011). An
explanation for this relationship is that students who are open to new concepts and
experiences, intellectually curious, and actively process the information provided
and relate it to their personal experiences, which enhances AP.
In addition, Hazrati-Viari, Rad, and Torabi (2012) found that academic motivation
mediates the effect between conscientiousness and AP, and between openness to
experience and AP. This further supports the idea that personality traits promote AP
through predisposing students towards academic behaviors that are conducive to
performance through other constructs, such as motivation and learning approaches.
Having a clear insight about academic preferences and being confident in one’s
skills within a particular domain can boost motivation and promote efforts when
dealing with academic demands. In fact, students high on academic self-concept
(i.e., beliefs, attitudes, and perception towards their skills and performance) are more
intrinsically motivated, which can enhance AP (Khalaila, 2015).
General and Academic Self-Efficacy
Yusuf (2011) reported that self-efficacy has a direct effect on the students’ academic
motivation and tendency to engage in self-learning, which indirectly increases
their AP at university. Similarly, self-efficacy had the strongest indirect effect on
AP through promoting effective studying strategies, namely deep processing, and
other non-cognitive variables, such as achievement goals. Such a strong indirect
contribution indicates that students’ belief in their academic skills might help direct
their cognition towards trying to understand complex ideas using prior knowledge
and making interconnections among them (Fenollar, Roman, & Cuestas, 2007).
Students with high self-efficacy tend to be also more academic motivated (Gota,
2012), which, as discussed earlier, has a positive impact on AP. Furthermore,
students who believe that they are capable of achieving are better in regulating the
effort exerted for academic achievement. Also, these students tend to believe that
intelligence is changeable and depends on the effort placed, contrary to students
with low self-efficacy, who tend to believe that intelligence is innate and resilient
to change. As such, high self-efficacy students are better at controlling natural
impulses, such as being distracted or giving up, across various academic demands
ranging from dull to very demanding tasks. Moreover, self-efficacy is associated
with efficient goal setting, which includes engaging in challenging tasks, gaining
new information, and performing better at univeristy (Komarraju & Nadler, 2013).
Putwain, Sander, and Larkin (2013) found a positive indirect effect of academic
self-efficacy on AP via pleasant emotion-related constructs, such as hope, enjoyment
and pride. These findings imply that academic self-efficacy may impact the student’s
overall well-being, and that could be a drive for them to reach academic outcomes.
Academic self-efficacy was directly related to parenting styles, whereby students
who perceived their parents as authoritative had higher academic self-efficacy than
M.-J. SANCHEZ-RUIZ ET AL.
74
those who perceived their parents as non-authoritative, which, in turn, resulted in a
higher AP of the former (Gota, 2012).
Trait EI and Emotion-Related Constructs
Previous studies have shown that emotional self-efficacy has an impact on academic
self-efficacy, and indirectly enhances AP (Adeyemo, 2007; Hen & Goroshit,
2014), which suggests the importance of the affective component of personality in
educational contexts. A study conducted by Sanchez-Ruiz (in progress) found that
trait EI indirectly predicted AP in undergraduates through procrastination (negative
relationship) and major satisfaction (positive relationship).
In a study conducted by Austin et al. (2005) trait EI mediated the association
between gender and exam performance among medical students. Additionally,
females scored higher on trait EI, which could be a potential partial explanation
of previous findings (e.g. Ferguson et al. 2002) where females performed better
in medical school than males. The mediation effect of trait EI between gender and
AP could act as a protective factor against academic stress. More recently, Perera
and DiGiacomo (2015) reported two novel two-step pathways by which trait EI
indirectly contributed to AP. In the first pathway, trait EI impacts AP through the
perceived social support, which subsequently increases students’ positive affect, in
turn, increasing AP. In the second pathway, trait EI influences AP through adaptive
academic strategies, namely active coping, positive reinterpretation, and planning,
which also increased the students’ academic engagement. Similar research is being
carried out investigating the indirect effects of trait EI on medical AP via parental
support, coping skills and academic stress (e.g., Chatila. et al., in progress).
LIMITATIONS OF THE EXISTING LITERATURE
Overemphasis on Cognitive and Traditional Personality Traits
As we have mentioned, while cognitive factors play a major role in predicting
AP, there are other factors, specifically non-cognitive, which are equally or more
important predictors. It may be then problematic to rely extensively on cognitive
factors in predicting AP in higher education settings. This is especially the case
because universities criteria for student admissions have become increasingly
demanding, and thus, the selected students have high scores on intelligence and
aptitude tests and a restriction of range in intelligence (Johnson, 2003). The role of
intelligence in AP (as measured by IQ tests), might be more prominent for particular
academic majors, such as those that require logical reasoning (Sanchez-Ruiz,
Mavroveli, & Poullis, 2013).
Studies on the incremental validity of non-cognitive over cognitive factors in
the prediction of AP is key, but so far it has been mainly focused on traditional
personality traits. It would be advisable for future research to study how specific
NON-COGNITIVE VARIABLES AND ACADEMIC ACHIEVEMENT
75
constructs such as general, academic and emotional self-efficacy, perfectionism and
fear of failure, can predict AP over and above cognitive variables.
In sum, future research could consider the restriction of range in cognitive abilities
in higher education, the potential domain-specificity of the relationship between AP
and cognitive and non-cognitive factors, and the incremental validity of specific
traits.
Lack of Cross-Cultural Research
Despite the existence of some studies examining predictors of AP across different
ethnic groups (e.g., Duong, Badaly, Liu, Schwartz, & McCarty, 2015; Woolf,
Potts, & McManus, 2011), these studies have been mainly conducted in Western
cultures. Lack of cross-cultural research limits researchers’ ability to understand
how AP is conceptualized and assessed across different cultures and academic
systems, thus inhibiting the ability to draw generalisable conclusions about the
predictors of AP.
In the first systematic cross-cultural meta-analysis of its kind, Dekker and Fischer
(2008) highlighted the role of culture on academic achievement goals, which have
clear repercussions on AP, and the reason behind those goals across cultures. Their
findings suggest that social context has a moderately significant effect on adopting
academic achievement goals. For instance, individuals in cultures that value
embeddedness (i.e., social cohesion) exhibited a desire for gaining social approval
through demonstrating their competence and skills. Distinctively, in egalitarian
cultures, individuals demonstrate high achievement motivation due to a desire to
master challenging tasks (Dekker & Fischer, 2008).
Excessive Focus on GPA
The present chapter has reviewed research studies using mainly GPA scores as
indicators of AP. While GPA has been widely used as a proxy for AP, it is not free
from limitations. First, there is the potential problem of grade inflation, which can
also occur differentially by instructor and subject (e.g., Johnson, 2003; Young,
2003), and can result in scores not truly representing academic achievement. Also,
the diversity in grading systems across various institutions (e.g., percentage grading
system vs. GPA) further complicates the interpretation of results (Didier, Kreiter,
Buri, & Solow, 2006).
At the individual level, using university grades as the only indicator of AP has
multiple disadvantages. One disadvantage lies in the high stakes status of GPA and
entry exams for the academic and work opportunities of students where pressures
to pass can negatively impact their performance on these exams (Karatas, Alci, &
Aydin, 2013). In addition to the stress and pressure students might feel as they are
determining their future, there are environmental and internal factors that can affect
performance on exams that may exist occasionally or at one point in time only, such
M.-J. SANCHEZ-RUIZ ET AL.
76
as, time of the exam (Burns, 2004), mood (Febrilia & Warokka, 2011), and sleep
quality (Gilbert & Weaver, 2010).
In tertiary and pre-tertiary education, there is very often a major interest in
preparing students for particular assessments that determine promotion (e.g., SAT
exams in the US, UMAT exam for medical education in Australia and New Zealand).
The focus on teaching to test, therefore, greatly limits the quality of learning
experiences because the primary educational focus is almost exclusively on covering
the material for the specific test (Atkinson & Geiser, 2009).
Some researchers argue that standards-based assessment, which measure skills
(or competences) using particular outcomes is more informative than the GPA
scores, which might simply evaluate students’ recollection of what is covered in a
given course or curriculum (Nicholson, 2014; Stiggins, 2005). Also, outcomes of
standards-based assessments, which are framed within normative standards, are more
comparable across different courses and departments than GPA scores (Tam, 2014).
Additionally, it is contended that standards-based assessment promotes a sense of
justice among students (Tognolini & Stanley, 2007; Wilkinson, Wells, & Bushnell,
2007); given that standards are grounded in task mastery as opposed to social norms,
every student who attains these competences receives good evaluations, which is not
necessarily the case for university GPA.
Still, many of the abovementioned criticisms of GPA can be applied to this type
of assessment, such as the influence of students’ anxiety due to pressures on exam
performance, and the tendency to direct great educational efforts to help students
perform well on such tests. Both GPA and standard-based tests are summative
assessments. Much less effort has been put into the investigation of cognitive and
non-cognitive factors involved in students’ individual performance on formative
assessment.
Overlooking Key Components of Learning
One way we can classify the assessment of academic performance is into summative
assessment (primarily focused on “summing” up what a student has learned over
the course of the curriculum) and formative assessment (primarily focused on
understanding and informing the process of learning; Berry & Adamson, 2011).
Overemphasis on university GPA, entry scores and standard-based assessment can
promote surface approaches to learning, or learning to mainly pass exams. When the
bulk of the assessment is summative, students tend to work towards obtaining good
grades, so they tend to utilize surface approaches to learning (e.g., memorization
and other strategies for recitation or reproduction of knowledge) and are likely to
be driven by extrinsic motivation (Marton & Säljö, 1976). In this context, grades
become a very limited measure of learning that focuses on the final outcome of a
complex process. Thus, the non-cognitive factors influencing grades might not be
the same as those influencing different aspects of the learning process.
NON-COGNITIVE VARIABLES AND ACADEMIC ACHIEVEMENT
77
When the assessment is formative, namely when it aims at monitoring the
learning process to be able to modify the teaching and learning experiences to
promote academic success, the non-cognitive factors contributing to performance
can be very different, and can be used to better understand learners’ approaches to
learning effectively.
Watkins, Carnell, and Lodge (2007) identified four dimensions of effective
learning. The first dimension is active learning, which refers to a cycle of Do-
Review-Learn-Apply developed by Dennison and Kirk (1990). Learners first
produce work that is then reviewed with feedback on how to improve it, then they
are given the opportunity to incorporate this feedback as part of their work. The
second dimension is collaborative learning where learners produce individual or
group work that can only be done with the continuous input of peers. In the third
dimension, learners make choices about their learning, this is otherwise referred to
as autonomous learning. They have a say in what they learn, how they learn it and
how they think would best assess their learning. Cconsequently, motivation to learn
transforms from extrinsic (i.e. grades) to intrinsic (i.e. curiosity, will to improve
and discover). The fourth dimension, meta-learning, requires that learners monitor
and review how they learn. They first reflect on what helped them learn best and
the barriers that made learning difficult. Second, they think of things they can do to
address the barriers and, then, take action.
However, to our knowledge, the literature relating non-cognitive factors as
predictors of effective learning is scarce. Some studies have identified a link
between active learning strategies and AP. For instance, Fayombo (2013) found
that active learning strategies (e.g., class discussion, video clips, role-playing, five-
minute reflective papers, and clarification pauses) explained 22% of the variance
in AP. Other studies have illustrated the role that collaborative learning plays in
academic engagement and motivation through processes such as peer support and
acceptance (e.g., Wentzel & Watkins, 2002). Still, approaches to effective learning
and their relationships to various non-cognitive variables (e.g., personality traits,
academic motivation, and self-efficacy) remain largely unexplored. Findings on
how personality and emotion-related traits influence approaches to using feedback,
collaborating with other and learning about one’s learning could inform teachers’
approaches in supporting learners to better regulate particular traits that could
be hindering their learning. The following section presents some potential future
directions to be undertaken by researchers.
Future directions. Future studies could focus on the impact of extraversion and
the social components of trait EI on collaborative learning. In addition, the ability
of making choices while learning (i.e., autonomous learning) could be related to
openness to experience and intrinsic motivation. Approaches to meta-learning might
be influenced by degrees of conscientiousness. In addition, the effective learning
components themselves could have an impact on some non-cognitive factors. For
M.-J. SANCHEZ-RUIZ ET AL.
78
example, receiving continuous feedback as part of active learning could promote
self-efficacy among students.
Educational Implications
In academic settings, the assessment and understanding of individual differences in
noncognitive variables is essential for the planning and implementation of emotional
education initiatives (Vandervoort, 2006). Education professionals and academic
and career counseling practitioners, and most importantly, students, could use the
findings on personality and emotion-related factors of AP to cater for students’ needs
and assist them with decisions and planning, as well as dealing with problems of
academic engagement and satisfaction.
As for personality, this chapter has reviewed some literature indicating that,
for example, extraverts are more likely to underperform because of the time spent
socializing. In addition, neuroticism can be associated with test anxiety, which might
hinder the AP. Moreover, a possible explanation to the findings on openness is that
students who score high on this trait might be more intellectually curious and seek
to learn new information. Furthermore, as mentioned above, those personality traits
might relate to AP differently across academic disciplines. These findings can be
informative for teachers when deciding on the teaching and learning strategies that
are more efficient for particular students and how to enhance their motivation in the
classroom.
In the case of academic motivation, as reviewed earlier, according to the SDT
theory, intrinsic motivation can be achieved by the satisfaction of basic needs for
autonomy and competence. Education professionals have an important role in
promoting self-determination by using autonomy-supportive approaches when
introducing learning tasks and by fostering pleasure and satisfaction at university.
However, much often, educators may minimize the role of intrinsic motivation by
introducing external conditions (e.g., grades, and reinforcement) to achievement and
learning, which may in turn outweigh the role of extrinsic motivation, and decrease
enthusiasm and genuine interest in the process of learning.
Several intervention programs have aimed to increase students motivation
through various methodologies. In fact, a meta-analysis on academic motivation
enhancement interventions showed the effectiveness of such interventions
(Wagner & Szamoskozi, 2012). One of the successful interventions on teachers
adopted a multidisciplinary approach to enhance student’s motivation and interest
(Bartimote-Aufflick, Walker, Smith, Sharma, Collier, & George, 2009). Another
program, Possible Selves Program, focused on the improvement of personal and
academic motivation from elementary school through post-secondary education.
By focusing on ideas on what one might become in the future, this program was
effective in increasing athlete university students’ motivation, performance, and
retention (Hock, Deshler, & Schumaker, 2006).
NON-COGNITIVE VARIABLES AND ACADEMIC ACHIEVEMENT
79
As opposed to the trait approach to personality, which views personality as
relatively stable and fixed across the life-span, the social-cognitive theory suggests
that self-efficacy is subject to enhancement and personal development through various
strategies, including repeated experiences of success, receiving encouragement from
others, seeing efficacious behaviour from others, and having a healthy physical state
(Bandura, 1997). In fact, an experimental study investigating the effectiveness of an
individual cognitive-behavioral intervention (Bresó, Schaufeli, & Salanova, 2011)
found that academic self-efficacy, as well as AP and engagement increased after the
intervention. This suggests that self-efficacy can be modified to benefit educational
outcomes both directly, or through its effect on other variables.
While it is widely accepted that academic motivation and self-efficacy can be
enhanced among students through educational programs and interventions, the
training of trait EI for educational purposes is somehow more controversial due
to the enduring and stable nature of personality traits. However, great progress has
been made regarding emotional education in general (Vandervoort, 2006), and the
development of particular trait EI aspects through treatment. An intervention study
reported an increase in trait EI and certain EI-related constructs, namely emotion
identification and emotion management compared to a control group, who did not
receive the training (e.g., Nelis, Quoidbach, Mikolajczak, & Hansenne, 2009). A
similar intervention program focused on emotional competence not only showed
significant increase in emotion-related aspects (e.g., identifying, understanding,
expressing and managing emotions), but also showed a subsequent increase in life
satisfaction, well-being, physical and mental health, global social function, and
employability, as well as a decrease on neuroticism level among individuals who
received the training.
It is noteworthy that extremely high trait EI can also contribute to maladaptive
behaviors (see Petrides et al., under review), which should be taken into consideration
by educators, who can use the trait EI approach to develop high quality relationships
with students, and using previous knowledge to be able to distinctions genuine
students’ emotions for non-genuine ones that can also contribute to the student-
teacher dynamics; thus, increasing students’ performance (Roy, 2015).
In sum, educators can benefit from the growing understanding of the dynamic
relationships, direct and indirect, between non-cognitive factors and AP, by
developing interventions and designing curricula that empowers students as learners
and enhances their intrinsic motivation, academic and emotional self-efficacy
in a myriad of domains and ensure their optimal academic success. In addition,
educational and career counsellors may find it useful to assess and account for the
role of noncognitive factors such as academic and emotional self-efficacy when
advising students on academic matters. It is important not to misuse findings on
non-academic predictors of AP. The aim should not be to encourage learners to avoid
certain approaches that they might find difficult or conflicting with their personal
characteristics and overfocus on those that they find congruent with their traits.
M.-J. SANCHEZ-RUIZ ET AL.
80
REFERENCES
Adeyemo, D. A. (2007). Moderating influence of emotional intelligence on the link between academic
self-efficacy and achievement of university students. Psychology & Developing Societies, 19(2),
199–213.
Amrai, K., Motlagh, S. E., Zalani, H. A., & Parhon, H. (2011). The relationship between academic
motivation and academic achievement students. Procedia-Social and Behavioral Sciences, 15,
399–402.
Atkinson, R. C., & Geiser, S. (2009). Reflections on a century of college admissions tests. Educational
Researcher, 38(9), 665–676.
Austin, E. J., Evans, P., Goldwater, R., & Potter, V. (2005). A preliminary study of emotional intelligence,
empathy and exam performance in first year medical students. Personality and Individual Differences,
39(8), 1395–1405.
Ayyash-Abdo, H., & Sánchez-Ruiz, M. J. (2012). Subjective well-being and its relationship with academic
achievement and multilinguality among Lebanese university students. International Journal of
Psychology, 47(3), 192–202.
Baker, S. R. (2004). Intrinsic, extrinsic, and amotivational orientations: Their role in university adjustment,
stress, well-being, and subsequent academic performance. Current Psychology, 23(3), 189–202.
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review,
84(2), 191–215.
Bandura, A. (1995). Self-efficacy in changing societies. New York, NY: Cambridge University Press.
Bandura, A. (1997). Self-efficacy: The exercise of control. New York, NY: Freeman.
Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (1996). Multifaceted impact of self-efficacy
beliefs on academic functioning. Child Development, 67(3), 1206–1222.
Bartimote-Aufflick, K., Walker, R., Smith, L., Sharma, M., Collier, M., & George, A. (2009, August).
A multidisciplinary approach to improving university student motivation and interest (pp. 25–29).
Biennial conference of the European Association for Research on Learning and Instruction,
Amsterdam.
Berry, R., & Adamson, B. (2011). Assessment reform: past, present and future. In Assessment reform in
education (pp. 3–14). Amsterdam: Springer Netherlands.
Brody, N. (2004). What cognitive intelligence is and what emotional intelligence is not. Psychological
Inquiry, 15, 234–238.
Burns, D. J. (2004). Anxiety at the time of the final exam: relationships with expectations and performance.
Journal of Education for Business, 80(2), 119–124.
Carroll, J. B. (1993). Human cognitive abilities: A survey of factor-analytic studies. New York, NY:
Cambridge University Press.
Cerasoli, C. P., Nicklin, J. M., & Ford, M. T. (2014). Intrinsic motivation and extrinsic incentives jointly
predict performance: A 40-year meta-analysis. Psychological Bulletin, 140(4), 980.
Chamorro-Premuzic, T., & Arteche, A. (2008). Intellectual competence and academic performance:
Preliminary validation of a model. Intelligence, 36(6), 564–573.
Chamorro-Premuzic, T., & Furnham, A. (2003). Personality predicts academic performance: Evidence
from two longitudinal university samples. Journal of Research in Personality, 37(4), 319–338.
Chamorro-Premuzic, T., & Furnham, A. (2003). Personality traits and academic examination performance.
European Journal of Personality, 17(3), 237–250.
Chamorro-Premuzic, T., & Furnham, A. (2004). A possible model for understanding the personality-
intelligence interface. British Journal of Psychology, 95(2), 249–264.
Chamorro-Premuzic, T., & Furnham, A. (2008). Personality, intelligence and approaches to learning as
predictors of academic performance. Personality and Individual Differences, 44(7), 1596–1603.
Chatila, R., Khoury, J., Faour, W., & Sanchez-Ruiz, M. J. (in progress). A model of socio-emotional
behavioral-academic and personality predictors of academic performance in medical students.
Chemers, M. M., Hu, L. T., & Garcia, B. F. (2001). Academic self-efficacy and first year college student
performance and adjustment. Journal of Educational Psychology, 93(1), 55–64.
NON-COGNITIVE VARIABLES AND ACADEMIC ACHIEVEMENT
81
Corker, K. S., Oswald, F. L., & Donnellan, M. B. (2012). Conscientiousness in the classroom: A process
explanation. Journal of Personality, 80(4), 995–1028.
Costa, P. T., & McCrae, R. R. (1992). Normal personality assessment in clinical practice: The NEO
personality inventory. Psychological Assessment, 4(1), 5–13.
Daoust, H., Vallerand, R. J., & Blais, M. R. (1988). Motivation and education: A look at some important
consequences. Canadian Psychology, 29(2a), 172.
Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-
determination perspective. Educational psychologist, 26(3–4), 325–346.
Dekker, S., & Fischer, R. (2008). Cultural differences in academic motivation goals: A meta-analysis
across 13 societies. The Journal of Educational Research, 102(2), 99–110.
Dennison, B., & Kirk, R. (1990). Do, review, apply: A simple guide to experimental learning. Oxford:
Blackwell Education.
DeYoung, C. G., Hirsh, J. B., Shane, M. S., Papademetris, X., Rajeevan, N., & Gray, J. R. (2010). Testing
predictions from personality neuroscience: brain structure and the big five. Psychological Science,
21(6), 820–828.
Didier, T., Kreiter, C. D., Buri, R., & Solow, C. (2006). Investigating the utility of a GPA institutional
adjustment index. Advances in health sciences education, 11(2), 145–153.
Di Domenico, S. I., & Fournier, M. A. (2015). Able, ready, and willing: Examining the additive and
interactive effects of intelligence, conscientiousness, and autonomous motivation on undergraduate
academic performance. Learning and Individual Differences, 40, 156–162.
Diseth, A. (2013). Personality as an indirect predictor of academic achievement via student course
experience and approach to learning. Social Behavior and Personality, 41(8), 1297–1308.
Duong, M. T., Badaly, D., Liu, F. F., Schwartz, D., & McCarty, C. A. (2015). Generational differences
in academic achievement among immigrant youths a meta-analytic review. Review of Educational
Research.
Fallahzadeh, H. (2011). The relationship between emotional intelligence and academic achievement in
medical science students in Iran. Procedia-Social and Behavioral Sciences, 30, 1461–1466.
Fayombo, G. A. (2013). Active learning strategies and academic achievement among some psychology
undergraduates in Barbados. International Scholarly and Scientific Research & Innovation, 7(7),
967–971.
Febrilia, I., Warokka, A., Abdullah, H. H., & Indonesia, C. (2011). University students’ emotional state
and academic performance: New insights of managing complex cognitive. Journal of e-Learning &
Higher Education, 2011, b1–b15.
Fenollar, P., Román, S., & Cuestas, P. J. (2007). University students’ academic performance: An
integrative conceptual framework and empirical analysis. British Journal of Educational Psychology,
77(4), 873–891.
Ferguson, E., James, D., & Madeley, L. (2002). Factors associated with success in medical school:
Systematic review of the literature. BMJ, 324(7343), 952–957.
Freudenthaler, H. H., Neubauer, A. C., Gabler, P., Scherl, W. G., & Rindermann, H. (2008). Testing
and validating the trait emotional intelligence questionnaire (TEIQue) in a German-speaking sample.
Personality and Individual Differences, 45(7), 673–678.
Furnham, A., & Chamorro-Premuzic, T. (2004). Personality and intelligence as predictors of statistics
examination grades. Personality and Individual Differences, 37(5), 943–955.
Furnham, A., & Monsen, J. (2009). Personality traits and intelligence predict academic school grades.
Learning and Individual Differences, 19(1), 28–33.
Furnham, A., Chamorro-Premuzic, T., & McDougall, F. (2002). Personality, cognitive ability, and beliefs
about intelligence as predictors of academic performance. Learning and Individual Differences, 14(1),
47–64.
Furnham, A., Nuygards, S., & Chamorro-Premuzic, T. (2013). Personality, assessment methods and
academic performance. Instructional Science, 41(5), 975–987.
Gilbert, S. P., & Weaver, C. C. (2010). Sleep quality and academic performance in university students:
A wake-up call for college psychologists. Journal of College Student Psychotherapy, 24(4), 295–306.
M.-J. SANCHEZ-RUIZ ET AL.
82
Gota, A. A. (2012). Effects of parenting styles, academic self-efficacy, and achievement motivation on
the academic achievement of university students in Ethiopia. Perth, Western Australia: Edith Cowan
University.
Hazrati-Viari, A., Rad, A. T., & Torabi, S. S. (2012). The effect of personality traits on academic
performance: The mediating role of academic motivation. Procedia-Social and Behavioral Sciences,
32, 367–371.
Hen, M., & Goroshit, M. (2014). Academic self-efficacy, emotional intelligence, GPA and academic
procrastination in higher education. Eurasian Journal of Social Sciences, 2(1), 1–10.
Hock, M. F., Deshler, D. D., Schumaker, J. B., Dunkel, C., & Kerpelman, J. (2006). Enhancing student
motivation through the pursuit of possible selves. In C. Dunkel & J. Kerpelman (Eds.), Possible
selves: Theory, research, and applications (pp. 205–221). New York, NY: Nova Science Publishers,
Inc.
Johnson, V. E. (2003). Grade inflation: A crisis in college education. New York, NY: Springer-Verlag.
Kappe, R., & van der Flier, H. (2010). Using multiple and specific criteria to assess the predictive validity
of the Big Five personality factors on academic performance. Journal of Research in Personality,
44(1), 142–145.
Karatas, H., Alci, B., & Aydin, H. (2013). Correlation among high school senior students test anxiety,
academic performance and points of university entrance exam. Educational Research Reviews, 8,
919–926.
Khalaila, R. (2015). The relationship between academic self-concept, intrinsic motivation, test anxiety,
and academic achievement among nursing students: Mediating and moderating effects. Nurse
Education Today, 35(3), 432–438.
Khan, A. (2013). Predictors of positive psychological strengths and subjective well-being among North
Indian adolescents: Role of mentoring and educational encouragement. Social Indicators Research,
114(3), 1285–1293.
Komarraju, M., & Nadler, D. (2013). Self-efficacy and academic achievement: Why do implicit beliefs,
goals, and effort regulation matter? Learning and Individual Differences, 25, 67–72.
Komarraju, M., Karau, S. J., Schmeck, R. R., & Avdic, A. (2011). The Big Five personality traits, learning
styles, and academic achievement. Personality and Individual Differences, 51(4), 472–477.
Kuncel, N. R., & Hezlett, S. A. (2010). Fact and fiction in cognitive ability testing for admissions and
hiring decisions. Current Directions in Psychological Science, 19(6), 339–345.
Lawler, B. (2012). Academic performance beyond cognitive ability: Personality, emotional intelligence
and self-efficacy–a mixed cross-sectional and longitudinal study. Liverpool: Liverpool John Moores
University.
Lee, W., Lee, M. J., & Bong, M. (2014). Testing interest and self-efficacy as predictors of academic self-
regulation and achievement. Contemporary Educational Psychology, 39(2), 86–99.
Lounsbury, J. W., Sundstrom, E., Loveland, J. M., & Gibson, L. W. (2003). Intelligence,“Big Five”
personality traits, and work drive as predictors of course grade. Personality and Individual Differences,
35(6), 1231–1239.
Marton, F., & Säljö, R. (1976). On qualitative differences in learning. I: Outcome and process. British
Journal of Educational Psychology, 46, 4–11.
Matthews, G., Deary, I. J., & Whiteman, M. C. (2009). Personality traits (3rd ed.). New York, NY:
Cambridge University Press.
Mavroveli, S., & Sánchez-Ruiz, M. J. (2011). Trait emotional intelligence influences on academic
achievement and school behaviour. British Journal of Educational Psychology, 81(1), 112–134.
Mavroveli, S., Petrides, K. V., Shove, C., & Whitehead, A. (2008). Investigation of the construct of trait
emotional intelligence in children. European Child & Adolescent Psychiatry, 17(8), 516–526.
McCrae, R. R., & Costa Jr, P. T. (1997). Personality trait structure as a human universal. American
Psychologist, 52(5), 509–516.
Mikolajczak, M., Luminet, O., Leroy, C., & Roy, E. (2007). Psychometric properties of the trait emotional
intelligence questionnaire: Factor structure, reliability, construct, and incremental validity in a French-
speaking population. Journal of Personality Assessment, 88(3), 338–353.
NON-COGNITIVE VARIABLES AND ACADEMIC ACHIEVEMENT
83
Multon, K. D., Brown, S. D., & Lent, R. W. (1991). Relation of self-efficacy beliefs to academic outcomes:
A meta-analytic investigation. Journal of Counseling Psychology, 38(1), 30–38.
Nelis, D., Quoidbach, J., Mikolajczak, M., & Hansenne, M. (2009). Increasing emotional intelligence:
(How) Is it possible? Personality and Individual Differences, 47(1), 36–41.
Newsome, S., Day, A. L., & Catano, V. M. (2000). Assessing the predictive validity of emotional
intelligence. Personality and Individual Differences, 29(6), 1005–1016.
Nicholson, T. (2014). Standards-based assessment. In S. Little & A. Akin-Little (Eds.), Academic
assessment and intervention (pp. 316–337). New York, NY: Routledge.
Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom applying
self-determination theory to educational practice. Theory and Research in Education, 7(2), 133–144.
Noftle, E. E., & Robins, R. W. (2007). Personality predictors of academic outcomes: Big five correlates of
GPA and SAT scores. Journal of Personality and Social Psychology, 93(1), 116–130.
Parker, J. D., Summerfeldt, L. J., Hogan, M. J., & Majeski, S. A. (2004). Emotional intelligence and
academic success: Examining the transition from high school to university. Personality and individual
differences, 36(1), 163–172.
Perera, H. N., & DiGiacomo, M. (2013). The relationship of trait emotional intelligence with academic
performance: A meta-analytic review. Learning and Individual Differences, 28, 20–33.
Perera, H. N., & DiGiacomo, M. (2015). The role of trait emotional intelligence in academic performance
during the university transition: An integrative model of mediation via social support, coping, and
adjustment. Personality and Individual Differences, 83, 208–213.
Pérez-González, J. C., & Sanchez-Ruiz, M. J. (2014). Trait emotional intelligence anchored within the
Big Five, Big Two and Big One frameworks. Personality and Individual Differences, 65, 53–58.
Perez-Gonzalez, J. C., Petrides, K. V., & Furnham, A. (2005). Measuring trait emotional intelligence. In
R. Schulze & R. D. Roberts (Eds.), International handbook of emotional intelligence (pp. 181–201).
Cambridge, MA: Hogrefe & Huber.
Petrides, K. V. (2009). Psychometric properties of the trait emotional intelligence questionnaire (TEIQue).
In Assessing emotional intelligence (pp. 85–101). New York, NY: Springer US.
Petrides, K. V. (2011). Ability and trait emotional intelligence. In T. Chamorro-Premuzic, S. von
Stumm, & A. Furnham (Eds.), The Wiley-Blackwell handbook of individual differences (pp. 656–678).
Hoboken, NJ: Blackwell Publishing Ltd.
Petrides, K. V. (2011). Emotional intelligence. The Wiley-Blackwell Handbook of Individual Differences,
656–678.
Petrides, K. V., Furnham, A., & Mavroveli, S. (2007). Trait emotional intelligence: Moving forward in the
field of EI. In G. Matthews, M. Zeidner, & R. D. Roberts (Eds.), Science of emotional intelligence:
Knowns and unknowns (pp. 151–166). New York, NY: Oxford University Press.
Petrides, K. V., Mikolajczak, M., Mavroveli, S., Sanchez-Ruiz, M. J., Furnham, A., & Perez-Gonzalez, J. C.
(under review). Developments in trait emotional intelligence research. Emotion Review.
Petrides, K. V., Pérez-González, J. C., & Furnham, A. (2007). On the criterion and incremental validity of
trait emotional intelligence. Cognition and Emotion, 21(1), 26–55.
Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance.
Psychological Bulletin, 135(2), 322–338.
Poropat, A. E. (2014). Other-rated personality and academic performance: Evidence and implications.
Learning and Individual Differences, 34, 24–32.
Putwain, D., Sander, P., & Larkin, D. (2013). Academic self-efficacy in study-related skills and
behaviours: Relations with learning-related emotions and academic success. British Journal of
Educational Psychology, 83(4), 633–650.
Richardson, M., Abraham, C., & Bond, R. (2012). Psychological correlates of university students’
academic performance: A systematic review and meta-analysis. Psychological bulletin, 138(2),
353–387.
Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., & Carlstrom, A. (2004). Do psychosocial and
study skill factors predict college outcomes? A meta-analysis. Psychological Bulletin, 130(2), 261.
Rohde, T. E., & Thompson, L. A. (2007). Predicting academic achievement with cognitive ability.
Intelligence, 35(1), 83–92.
M.-J. SANCHEZ-RUIZ ET AL.
84
Sánchez-Ruiz, M. J., Hernández-Torrano, D., Pérez-González, J. C., Batey, M., & Petrides, K. V.
(2011). The relationship between trait emotional intelligence and creativity across subject domains.
Motivation and Emotion, 35(4), 461–473.
Sanchez-Ruiz, M. J., Mavroveli, S., & Poullis, J. (2013). Trait emotional intelligence and its links to
university performance: An examination. Personality and Individual Differences, 54(5), 658–662.
Sánchez-Ruiz, M. J., Perez-Gonzalez, J. C., & Petrides, K. V. (2010). Trait emotional intelligence profiles
of students from different university faculties. Australian Journal of Psychology, 62(1), 51–57.
Sanchez-Ruiz, M. J., Pérez-González, J. C., Fayad, Y., Filella, G., & Soldevila, A. (in progress). Intellective
and non-intellective predictors of academic performance: From high school to university graduation.
Santesso, L. D., Dana, L. R., Schmidt, L. A., & Segalowitz, S. J. (2006). Frontal electroencephalogram
activation asymmetry, emotional intelligence, and externalizing behaviors in 10-year-old children.
Child Psychiatry and Human Development, 36(3), 311–328.
Sinha, D. (1966). A psychological analysis of some factors associated with success and failure in
university education: Intelligence, anxiety and adjustment of academic achievers and non-achievers.
Psychological Studies, 11(2), 69–88.
Spinath, B., Spinath, F. M., Harlaar, N., & Plomin, R. (2006). Predicting school achievement from general
cognitive ability, self-perceived ability, and intrinsic value. Intelligence, 34(4), 363–374.
Stiggins, R. (2005). From formative assessment to assessment for learning: A path to success in standards-
based schools. Phi Delta Kappan, 324–328.
Strenze, T. (2007). Intelligence and socioeconomic success: A meta-analytic review of longitudinal
research. Intelligence, 35(5), 401–426.
Strobel, M., Tumasjan, A., & Spörrle, M. (2011). Be yourself, believe in yourself, and be happy: Self-
efficacy as a mediator between personality factors and subjective well-being. Scandinavian Journal
of Psychology, 52(1), 43–48.
Tam, M. (2014). Outcomes-based approach to quality assessment and curriculum improvement in higher
education. Quality Assurance in Education, 22(2), 158–168.
Tognolini, J., & Stanley, G. (2007). Standards-based assessment: A tool and means to the development of
human capital and capacity building in education. Australian Journal of Education, 51(2), 129–145.
Touron, J. (1987). High school ranks and admission tests as predictors of first year medical students’
performance. Higher Education, 16(3), 257–266.
Trapmann, S., Hell, B., Hirn, J. O. W., & Schuler, H. (2007). Meta-analysis of the relationship between
the Big Five and academic success at university. Journal of Psychology, 215(2), 132–151.
Vaez, M., & Laflamme, L. (2008). Experienced stress, psychological symptoms, self-rated health and
academic achievement: A longitudinal study of Swedish university students. Social Behavior and
Personality, 36(2), 183–196.
Vandervoort, D. J. (2006). The importance of emotional intelligence in higher education. Current
Psychology, 25(1), 4–7.
Vecchione, M., Alessandri, G., & Marsicano, G. (2014). Academic motivation predicts educational
attainment: Does gender make a difference?. Learning and Individual Differences, 32, 124–131.
Vedel, A., Thomsen, D. K., & Larsen, L. (2015). Personality, academic majors and performance:
Revealing complex patterns. Personality and Individual Differences, 85, 69–76.
Wagner, E., & Szamosközi, S. (2012). Effects of direct academic motivation-enhancing intervention
programs: A meta-analysis. Journal of Evidence-Based Psychotherapies, 12(1), 85.
Watkins, C., Carnell, E., & Lodge, C. (2007). Effective learning in classrooms. London: Paul Chapman
Educational Publishing.
Wentzel, K. R., & Watkins, D. E. (2002). Peer relationsips and collaborative learning as contexts for
academic enablers. School Psychology Review, 31(3), 366.
Wilkinson, T. J., Wells, J. E., & Bushnell, J. A. (2007). What is the educational impact of standards-based
assessment in a medical degree? Medical Education, 41(6), 565–572.
Woolf, K., Potts, H. W., & McManus, I. C. (2011). Ethnicity and academic performance in UK trained
doctors and medical students: Systematic review and meta-analysis. BMJ, 342, 1–14.
Young, C. (2003). Grade inflation in higher education. ERIC Digest.
NON-COGNITIVE VARIABLES AND ACADEMIC ACHIEVEMENT
85
Yusuf, M. (2011). The impact of self-efficacy, achievement motivation, and self-regulated learning
strategies on students’ academic achievement. Procedia-Social and Behavioral Sciences, 15,
2623–2626.
Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary Educational
Psychology, 25(1), 82–91.
Maria-Jose Sanchez-Ruiz
Lebanese American University
Byblos, Lebanon
Jamil El Khoury
Lebanese American University
Byblos, Lebanon
George Saadé
Lebanese American University
Byblos, Lebanon
Miriam Salkhanian
Hopital Psychiatrique de la Croix
Lebanon
