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Aleksić, V., & Politis, P. (2023). Trait Emotional Intelligence and Multiple Intelligences as Predictors of Academic Success in
Serbian and Greek IT Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(2), 173-185.
Introduction
The academic success of university IT students has been monitored and studied for a long time,
but with the trend of increasing prices of education and new demands of the labor market, it is becoming
one of the key indicators for both school stakeholders and the economy in general. An aggregate of
academic achievement of IT students required for a successful future professional career can be reected
into subsets of specic skills, knowledge, and competencies in specic areas, such are digital literacy,
communication, collaboration, digital content creation, safety, and problem-solving (Vuorikari, Kluzer
and Punie, 2022) in which they should excel. The general attitude of researchers is that the individual
characteristics (qualities) of students are an obvious predictor of their success. When we think of individual
characteristics, we primarily think of their intelligence (Mayer, 2020). However, the results of many studies
show that general intelligence alone does not exceed 25% of the variance of success (Bergold and
Steinmayr, 2018), so other personal dispositions should also be taken into account. Student intelligence
combined with other personal dispositions can be observed as general ability which combined with
previous achievement researchers identied as the most consistent predictor of success (Richardson,
Abraham and Bond, 2012; Stankov and Lee, 2014).
Which IT student characteristics predict higher academic achievement and thus increase the
probability of a long-term successful career in the IT industry? To explore possible answers to this question,
the research focused on examining the role of trait emotional intelligence and multiple intelligences prole
as possible precursors of academic success. Besides traditional statics analysis methods, educational
data mining was used in the form of a articial neural network for predicting academic success. This
approach can provide clues on previously unknown trends that relate to student characteristics, behavior,
and academic performance (Mahajan and Saini, 2020). Identifying the relevance of the beforementioned
personality characteristics to academic achievement is important as it can inform the skills, knowledge,
and methods on which teachers and curriculum creators should focus while designing and implementing
the process of education.
The rest of the paper is organized as follows. The next section presents previous research on
Trait Emotional Intelligence and Multiple Intelligences as Predictors of
Academic Success in Serbian and Greek IT Students
Veljko Aleksić
1*
, Dionysios Politis
2
1
University of Kragujevac, Faculty of Technical Sciences, Čačak, Serbia, e-mail: veljko.aleksic@ftn.kg.ac.rs
2
Aristotle University of Thessaloniki, Department of Informatics, Thessaloniki, Greece, e-mail: dpolitis@csd.auth.gr
Abstract: Even though research on predicting the academic achievement of IT students is not scarce, the inclusion of trait
emotional intelligence and multiple intelligences as predictive factors is somewhat novel. The research examined associations
between identied proles of trait emotional intelligence and multiple intelligences, and academic success in the sample of 288
IT students, 208 from Serbia and 80 from Greece. The results show that trait emotional intelligence and multiple intelligences
prole both proved to be important predictors of academic success. Another predictor of IT students’ academic success was
related to their prior schooling success. The results indicate that fostering a student-centered learning model through tertiary
education, with special emphasis on students’ personal dispositions and traits, could be crucial for their academic success,
especially in the multidisciplinary eld of information technology.
Keywords: academic success; IT; trait emotional intelligence; multiple intelligences.
Original scientic paper
Received: February, 08.2023.
Revised: March, 16.2023.
Accepted: April, 29.2023.
UDK:
159.942.075-057.875:159.953(497.11+495)
10.23947/2334-8496-2023-11-2-173-185
© 2023 by the authors. This article is an open access article distributed under the terms and conditions of the
Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
*
Corresponding author: veljko.aleksic@ftn.kg.ac.rs
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Aleksić, V., & Politis, P. (2023). Trait Emotional Intelligence and Multiple Intelligences as Predictors of Academic Success in
Serbian and Greek IT Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(2), 173-185.
academic success prediction using trait emotional intelligence and the theory of multiple intelligences. The
research methodology is described in the following section. Results are then presented and discussed,
followed by concluding remarks.
Related work
Academic performance is heavily inuenced by biological and psychological traits independent
of standard notions of cognitive ability (Nye et al., 2012). Trait emotional intelligence concept concerns
individual belief about own emotions (Petrides and Mavroveli, 2018), but it is not a synonym with emotional
intelligence and should not be observed as a cognitive ability, competency, or skill (Siegling, Saklofske
and Petrides, 2015). Sanchez-Ruiz, Mavroveli and Poullis (2013) reported that personality characteristics
and trait emotional intelligence were better predictors of university students’ academic performance than
uid intelligence. Emotional experience is dened by the emotional intelligence model and by its nature
is a subjective one (Matthews, Zeidner and Roberts, 2008), thus not amenable to valid measurement
via self-assessment tools. The research on trait emotional intelligence signicantly expanded in the past
decade, including new assessment procedures by developing several questionnaires, their localization,
and psychometric evaluation (Dåderman and Kajonius, 2022; Herrera Torres, Buitrago Bonilla and
Cepero Espinosa, 2017; Jolić-Marjanović and Altaras-Dimitrijević, 2014; Pérez-Díaz and Petrides, 2021;
Stamatopoulou, Galanis and Prezerakos, 2016). These self-assessment questionnaires may also be used
as a diagnostic tool for identifying personality disorders as scores negatively correlate to most disorders
(Cuesta-Zamora, González-Martí and García-López, 2018; Sinclair and Feigenbaum, 2012). There are
four subdimensions (factors) of trait emotional intelligence: Well-Being, Self-Control, Emotionality, and
Sociability (Petrides, 2009). Trait emotional intelligence construct shows rigidity when compared to other
higher-order personality traits (Big Five) (McAdams, 1992; Soto and Jackson, 2013), revealing that
about 40% of the variance can be directly attributed to genetic factors and secondarily correlated to non-
shared environmental factors (Vernon et al., 2008). Self-motivation is often observed as a lower-order
trait, but its high levels directly lead to forming purposeful and achievement-oriented individuals which
in terms positively reect on superior academic success (Tepper, Duffy and Shaw, 2001). Students with
a higher level of self-control tend to avoid temptation-related external stimuli and are better at pursuing
established goals (Fujita, 2011). Researchers often point to the negative correlation between neuroticism
and academic performance as its higher level leads to negative emotions in a stressful situation, such
as evaluation (Martínez-Monteagudo et al., 2019). Taneja et al. (2020) emphasized that higher levels of
trait emotional intelligence positively inuenced student academic performance as their interaction with
peers and greater social interaction enable adaptive social functioning. Several recent studies reported
modest correlations on the samples of high-school and university students (Parker et al., 2004; Perera
and DiGiacomo, 2015) which aroused interest for further investigation and consequently nominated the
construct to be included in this research. Based on these considerations, even though the trait emotional
intelligence model was not constructed as a cognitive ability, it is expected to positively correlate with the
academic success of IT students.
The theory of multiple intelligences (Gardner, 1993) models a unique set of various personal
characteristics, eight in total (Gardner, 2000): musical/rhythmic, body/kinesthetic, logical/mathematical,
visual/spatial, verbal/linguistic, interpersonal, intrapersonal, and naturalist. Even though it was not initially
intended for educational application, a group of practitioners embraced the model and started adapting
their teaching practice to meet the perceived student individual capabilities and provide them with useful
and relevant information to gain more efcient learning. Multiple intelligences prole can also be used
for indirect assessment of various other students’ personal characteristics (Aleksić and Ivanović, 2017;
Sajjadi and De Troyer, 2022). The assessment procedure should be a part of the educational process
(Almeida et al., 2010) so that each intelligence type serves as a framework for cognitive and/or emotional
transfer (Bellarmen, 2021). However, even though over three decades have passed since introducing the
theory, there still is no valid practical alternative to self-assessment questionnaires. The fact that multiple
intelligence types all interact with one another to some degree, presents a challenge to prole validation.
Nevertheless, this interference also signies that multiple intelligences may impede one another so that
the model performs to its full potential. Each intelligence possesses clear and distinct cognitive-neural
correlates (Shearer, 2020). Various researchers explored the impact of multiple intelligences theory on
academic performance (Aguayo, Ruano and Vallejo, 2021; Liliawati, Zulkar and Kamal, 2018; Soleimani
et al., 2012; Šafranj and Zivlak, 2018). Yaghoob and Hossein (2016) observed the positive correlation
between verbal and visual intelligence with academic performance and reported on the relationship
between higher verbal, logical-mathematical, and intrapersonal intelligence and academic success. The
number of studies that focused on higher education academic success was particularly limited, which
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Aleksić, V., & Politis, P. (2023). Trait Emotional Intelligence and Multiple Intelligences as Predictors of Academic Success in
Serbian and Greek IT Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(2), 173-185.
further adds to the importance of this research.
Materials and Methods
The research problem is how IT students’ trait emotional intelligence and multiple intelligences
prole are related to academic success. The aim of the research is to examine the presumption that
the academic success of IT students can be predicted based on the assessed levels of trait emotional
intelligence factors and multiple intelligences prole. The results may inform whether trait emotional
intelligence and multiple intelligences prole assessment should be included as factors when designing
curriculum and delivering educational content to IT students.
Two research goals were dened:
1. Examination of the signicance of trait emotional intelligence factors predicting the academic
success of IT students.
2. Examination of the signicance of multiple intelligences prole predicting the academic success
of IT students.
In accordance with the dened aim and goals, two research hypotheses were formulated and are
further explained.
H1: The identied trait emotional intelligence factors are the predictors of IT students’ academic
success.
Rationale: The expectation is based on referent research (Laborde, Dosseville and Scelles, 2010)
that concluded the existence of a relationship between trait emotional intelligence scores and academic
performance.
H2: The identied multiple intelligences prole is the predictor of IT students’ academic success.
Rationale: The expectation is based on referent research (Gardner, 2000; Soleimani et al., 2012;
Yaghoob and Hossein, 2016) that concluded the existence of a relationship between multiple intelligences
prole and academic performance.
The rst part of the questionnaire was used to gather the basic sociodemographic information. The
following independent research variables were dened: Gender, Type of settlement, English prociency
level, Type of secondary school graduated, and Secondary education GPA. Following the hypotheses,
ve trait emotional intelligence factors (including global trait) were assessed in the second part of the
questionnaire via the TEIQue-SF psychometric instrument in the form of a 30-item seven-point Likert
type scale (Dåderman and Kajonius, 2022; Jolić-Marjanović and Altaras-Dimitrijević, 2014). The third part
of the questionnaire was the assessment of student multiple intelligences prole via IPVIS instrument
(Aleksić, & Ivanović, 2016) in the form of 119-item six-point Likert type scale. All the activities listed above
were time-restricted.
The research was realized in 2022 at the Faculty of Technical Sciences in Čačak, University of
Kragujevac (Serbia) and the School of Informatics, Aristotle University of Thessaloniki (Greece). A total
of 288 IT students 19 to 31 years of age participated in the research, out of which N = 208 (72.2 %)
were from Serbia, and N = 80 (27.8 %) were from Greece. The selection was made with the goal of
representing various geographic, economic, and socio-cultural environments. Students completed the
three-part questionnaire anonymously and voluntarily at the school facilities in about 60 minutes.
Following the theoretical-empirical nature of the research, and with the goal of exploring dened
hypotheses, the participants were examined by the descriptive-analytical non-experimental method,
based on which the distribution of properties was established and the relationships among variables were
analyzed. The statistical data analysis was performed using IBM SPSS Statistics v22 and IBM SPSS
Modeler v18 software packages. The following methods were used: descriptive statistics (frequency,
percentage, arithmetic mean, standard deviation, minimum, maximum, skewness, kurtosis), Shapiro-
Wilk test, correlation analysis, χ
2
test, Independent samples t-test, Cronbach’s alpha internal consistency
coefcient, Kaiser-Meyer-Olkin (KMO) measure of sample adequacy, Bartlett’s test, exploratory factor
analysis, analysis of variance (ANOVA), Tukey HSD test, Kruskal-Wallis H test, regression analysis, and
educational data mining.
Results
The valid sample of N = 288 students consisted of N = 143 (49.6 %) male and N = 65 (22.6 %)
female IT students from Serbia, and N = 64 (22.2 %) male and N = 16 (5.6 %) female IT students from
Greece. The average age of the participants was 21.1 years (SD = 1.28). In total, N = 173 (60.1 %)
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Aleksić, V., & Politis, P. (2023). Trait Emotional Intelligence and Multiple Intelligences as Predictors of Academic Success in
Serbian and Greek IT Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(2), 173-185.
students were living in urban areas (57.2 % in Serbia and 67.5 % in Greece) and N = 115 (39.9 %) were
living in rural areas (42.8 % in Serbia and 32.5 % in Greece). When asked about the perceived English
prociency level, N = 69 (24.0 %) reported advanced, N = 115 (39.9 %) reported upper intermediate, N
= 83 (28.8 %) reported intermediate, N = 20 (6.9 %) reported elementary, and N = 1 (0.3 %) reported
beginner. An independent samples t-test was conducted to examine whether there was a signicant
difference between IT students in relation to their English prociency level. The test revealed a signicant
difference between students t(286) = -4.37; p < .001. Students from Greece reported signicantly higher
English prociency level (M = 4.16, SD = .834) than students from Serbia (M = 3.66, SD = .880) on a scale
from 1 to 5.
The average secondary education GPA was 3.39 (SD = .753). An independent samples t-test
revealed no signicant difference for the average secondary education GPA between IT students in
Serbia and Greece. Most of the students graduated their secondary education in the eld of IT, computer
science or electrotechnics (N = 118; 41.0 %) (GPA = 3.44), N = 98 (34.0 %) graduated gymnasium (GPA =
3.37), N = 31 (10.8 %) graduated economy or law (GPA = 3.39), N = 13 (4.5 %) graduated in mechanical
engineering, trafc or construction (GPA = 3.23), and N = 28 (9.7 %) graduated some other vocational
school that was not listed in the questionnaire. An independent samples t-test was conducted to examine
whether there was a signicant gender difference between IT students concerning their secondary
education GPA. The test revealed a statistically signicant difference between students t(173.8) = -2.12,
p = .035. Females (M = 3.53, SD = .654) achieved signicantly higher secondary education GPA than
males (M = 3.34, SD = .783). There was no signicant effect on secondary education GPA for type of living
environment. A Pearson correlation coefcient was computed to assess the relationship between English
prociency level and secondary education GPA. There was no signicant association between the two
variables, r(286) = .18, p = .079.
Trait emotional intelligence factors
The inter-item correlation matrix for each of the ve TEIQue-SF factors (including global trait
emotional intelligence) shows the existence of statistically signicant correlations that conrmed the
validity of TEIQue-SF. The average inter-item correlation for each trait emotional intelligence was as
follows: well-being (.354), self-control (.207), emotionality (.186), and sociability (.087). The existence of a
positive statistically signicant correlations between individual traits and global trait emotional intelligence
in the range (.602÷.840) conrmed the construct validity (Clark, & Watson, 1995) of TEIQue-SF. The
internal consistencies for the scores in this study are presented in Table 1.
Table 1
Trait emotional intelligence rating
An independent samples t-test was conducted to examine whether there was a signicant gender
difference between IT students in relation to their trait emotional intelligence. The test revealed a signicant
gender difference between students in well-being trait t(286) = -2.23; p = .026 and emotionality trait t(286)
= -2.87; p = .004. Female students achieved signicantly higher scores in well-being trait (M = 70.9, SD =
16.9) than male students (M = 65.9, SD = 17.3). Female students also achieved signicantly higher scores
in emotionality trait (M = 65.4, SD = 18.3) than male students (M = 58.4, SD = 18.4). The t-test revealed
no signicant difference in self-control t(122.8) = 1.17, p = .244, sociability t(286) = -.021, p = .984, nor
global trait emotional intelligence t(286) = -1.77, p = .079.
An independent samples t-test was also conducted to examine whether there was a signicant
difference between IT students in relation to their country of origin. The test revealed a signicant
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Aleksić, V., & Politis, P. (2023). Trait Emotional Intelligence and Multiple Intelligences as Predictors of Academic Success in
Serbian and Greek IT Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(2), 173-185.
difference between students in well-being trait t(222.7) = 7.89; p < .001, self-control trait t(195.3) = 4.47; p
< .001, emotionality trait t(269.6) = 14.7; p < .001, sociability trait t(162.1) = 5.00; p < .001, and global trait
emotional intelligence t(199.8) = 12.5; p < .001. Students from Serbia achieved signicantly higher scores
than students from Greece in all trait emotional intelligence factors: well-being (M = 71.1, SD = 17.7) to (M
= 57.2, SD = 11.3), self-control (M = 59.0, SD = 16.6) to (M = 51.1, SD = 12.1), emotionality (M = 67.6, SD
= 15.2) to (M = 41.6, SD = 12.7), sociability (M = 57.7, SD = 13.7) to (M = 49.5, SD = 12.0), and global (M
= 64.6, SD = 11.6) to (M = 49.3, SD = 8.28).
The t-test revealed no signicant difference for type of living environment between IT students in
relation to their trait emotional intelligence.
A Pearson correlation coefcient was computed to assess the relationship between English
prociency level and trait emotional intelligence scores. There were very weak negative correlations
between the English prociency level and well-being trait score [r(286) = -.178; p = .002], emotionality
trait score [r(286) = -.174; p = .003], and global trait emotional intelligence score [r(286) = -.139; p =
.019]. Increases in the prociency level of English were correlated with decreases in well-being trait,
emotionality trait, and global trait emotional intelligence scores.
There were statistically signicant differences between groups when the effect of type of secondary
education school on trait emotional intelligence was compared, as determined by Kruskal-Wallis H test
for well-being [χ
2
(4) = 10.6, p = .032] with two highest mean rank scores of 188.8 for economy or law
and 144.4 for mechanical engineering, trafc or construction high schools, emotionality [χ
2
(4) = 19.1, p =
.001] with two highest mean rank scores of 186.8 for economy or law and 153.0 for IT, computer science
or electrotechnics high schools, and global trait emotional intelligence [χ
2
(4) = 15.9, p = .003] with two
highest mean rank scores of 194.5 for economy or law and 171.0 for mechanical engineering, trafc or
construction high schools.
A Pearson correlation coefcient was computed to assess the relationship between secondary
education GPA and trait emotional intelligence scores. There was no signicant correlation between
variables.
Multiple intelligences prole
IPVIS was found to be of excellent overall reliability (119 items; α=.952). The inter-item correlation
matrix for each of the eight factors shows the existence of statistically signicant correlations that
conrmed the validity of IPVIS. The average inter-item correlation for each of the multiple intelligences
was as follows: musical/rhythmic (.313), bodily/kinesthetic (.224), logical/mathematical (.277), visual/
spatial (.280), verbal/linguistic (.284), interpersonal (.166), intrapersonal (.303), and naturalist (.313). The
existence of positive statistically signicant average inter-item correlations in the range between .146
and .629 conrmed the internal consistency of IPVIS scales (Clark and Watson, 1995). The internal
consistencies for the scores in this study are presented in Table 2.
Table 2
Multiple intelligences prole score
An independent samples t-test were conducted to examine whether there was a signicant gender
difference between IT students in relation to their multiple intelligences prole scores. The t-test revealed
a signicant difference in naturalist intelligence, t(286) = -2.01, p = .045. Female students achieved
signicantly higher scores in naturalist intelligence (M = 66.1, SD = 17.8) than male students (M = 60.9,
SD = 20.3).
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Aleksić, V., & Politis, P. (2023). Trait Emotional Intelligence and Multiple Intelligences as Predictors of Academic Success in
Serbian and Greek IT Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(2), 173-185.
An independent samples t-test were also conducted to examine whether there was a signicant
difference between IT students from Serbia and Greece in relation to their multiple intelligences prole
scores. The t-test revealed a signicant difference in musical/rhythmic [t(286) = -3.29, p = .001],
intrapersonal [t(286) = 4.92, p < .001], and naturalist intelligence [t(286) = 5.49, p < .001]. While students
from Greece achieved signicantly higher scores in musical/rhythmic intelligence (M = 57.7, SD = 18.5)
than students from Serbia (M = 49.7, SD = 18.5), Serbian students exceeded their colleagues from Greece
in intrapersonal intelligence scores, (M = 74.6, SD = 14.9) and (M = 64.7, SD = 16.3), respectively, and
naturalist intelligence scores, (M = 66.1, SD = 19.0) and (M = 52.5, SD = 18.0), respectively.
When we examined whether there was a signicant difference for type of living environment between
IT students in relation to their multiple intelligences prole, the t-test revealed a signicant difference only
in naturalist intelligence, t(286) = -4.06, p < .001. Students living in rural areas (M = 68.0, SD = 18.9)
achieved signicantly higher scores in naturalist intelligence than students living in urban areas (M = 58.6,
SD = 19.4).
A Pearson correlation coefcient was computed to assess the relationship between English
prociency level and multiple intelligences prole. There was very weak positive correlation between
the English prociency level and musical/rhythmic intelligence (r(286) = .167; p=.005). Increases in the
prociency level of English were correlated with increases in musical/rhythmic intelligence score.
A one-way between-subjects ANOVA was conducted to compare the effect of the type of secondary
school student graduated on multiple intelligences prole. There was a signicant effect of type of secondary
school student graduated on musical/rhythmic and naturalist intelligence at the p < .05 level, [F(4,283) =
2.64, p = .034, η2 = .036] and [F(4,283) = 2.72, p = .030, η2 = .037], respectively. Post hoc comparisons
using the Tukey HSD test indicated that the level of musical/rhythmic intelligence was signicantly higher
in IT students who graduated gymnasium compared to students who graduated secondary education in
the eld of economy or law (p = .042), likewise for naturalist intelligence (p = .040).
A Pearson correlation coefcient was computed to assess the relationship between secondary
education GPA and multiple intelligences prole. There was very weak positive correlation between
secondary education GPA and logical/mathematical intelligence r(286) = .135, p = .022. Increases in
secondary education GPA were correlated with increases in logical/mathematical intelligence.
A Pearson correlation coefcient was computed to assess the relationship between trait emotional
intelligence and multiple intelligences prole and the result are presented in Table 3.
Table 3
Correlations between trait emotional intelligence factors and multiple intelligences prole
Weak and moderate positive correlations were identied between trait emotional intelligence factors
and multiple intelligences except for musical/rhythmic intelligence.
Predicting academic success
Students’ tertiary education GPA mean value was 7.72 (SD = .931). The t-test revealed no signicant
difference in gender between IT students concerning their tertiary education GPA t(286) = -.120, p = .905.
An independent samples t-test was conducted to examine whether there was a signicant
difference between IT students from Serbia and Greece in relation to their tertiary education GPA. The
test revealed a signicant difference between students t(286) = 4.63, p < .001. Students from Serbia
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Aleksić, V., & Politis, P. (2023). Trait Emotional Intelligence and Multiple Intelligences as Predictors of Academic Success in
Serbian and Greek IT Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(2), 173-185.
achieved signicantly higher GPA than students from Greece, (M = 7.87, SD =.897) and (M = 7.32, SD =
.907), respectively.
There was no signicant effect on tertiary GPA for the type of living environment, t(286) = .201, p
= .841.
A Pearson correlation coefcient was computed to assess the relationship between English
prociency level and tertiary education GPA. There was no signicant correlation between the two
variables, r(286) = .073, p = .218.
A one-way between-subjects ANOVA was conducted to compare the effect of the type of secondary
school student graduated on tertiary education GPA. There were no statistically signicant differences
between groups when the effect of type of secondary school was compared on tertiary education GPA
[F(4,283) = 2.08, p = .084].
A Pearson correlation coefcient was computed to assess the relationship between secondary
education GPA and tertiary education GPA. There was a signicant weak positive correlation between
the two variables, r(286) = .360, p < .001. Increases in secondary education GPA were correlated with
increases in tertiary education GPA.
A Pearson correlation coefcient was computed to assess the relationship between trait emotional
intelligence and tertiary education GPA. There were very weak positive correlations between tertiary
education GPA and emotionality trait [r(286) = .142; p = .016], sociability trait [r(286) = .126; p = .033], and
global trait emotional intelligence factors [r(286) = .142; p = .016]. Increases in the emotionality, sociability
or global trait emotional intelligence scores were correlated with increases in tertiary education GPA.
A Pearson correlation coefcient was also computed to assess the relationship between tertiary
education GPA and multiple intelligences prole. There were weak positive correlations between tertiary
education GPA and logical/mathematical [r(286) = .221, p < .001], intrapersonal [r(286) = .178, p = .002],
and naturalist intelligence [r(286) = .140, p = .017]. Increases in logical/mathematical, intrapersonal or
naturalist intelligence were correlated with increases in tertiary education GPA.
Educational data mining was used by performing predictive neural network analysis. A multiplayer
perception (i.e., MLP) class of articial neural network was used to build the model and test its accuracy.
The data was randomly assigned to training (70%), testing (20%) and validation (10%) subsets. All
covariates were normalized before the training. The scaled conjugate gradient method was used for the
batch training of the articial neural network. In order to obtain more accurate prediction, an ensemble
was created using boosting. The experimental model presented in Figure 1 was capable of calculating
predictor importance for tertiary education GPA based on the 19 input parameters (factors).
Figure 1. The experimental environment of the predictive neural network model.
MLP class of articial neural network identied N = 288 valid cases for processing, out of which N =
198 (68.7 %) was used for training, N = 44 (15.3 %) was used for testing, and N = 46 (16.0 %) was used
for validation, resulting in 96.9 % model accuracy. Architecture selection chose 3 nodes for the hidden
layer. The neural network model identied 17 predictors with signicant effects. The resulting accuracy
in the model summary was satisfactory 96.8 %. The relative importance of the predictors in the model is
visualized in Figure 2.
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Aleksić, V., & Politis, P. (2023). Trait Emotional Intelligence and Multiple Intelligences as Predictors of Academic Success in
Serbian and Greek IT Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(2), 173-185.
Figure 2. Predictor importance of the effects.
It should be noted that the values of the predictor importance are not the relative proportion of
the variable coefcients. These values only describe the effect on the tertiary education GPA if the input
variables change. However, the input variables were scaled differently.
Educational data mining provided far more optimized results compared to traditional factor analysis.
Out of the 17 identied predictors, the effects of secondary education GPA [β = .34, t(270) = 5.24, p <
.001], logical/mathematical intelligence [β = .02, t(270) = 3.92, p < .001], and the country that students live
[β = -.49, t(270) = -3.16, p = .002] were far stronger on tertiary education GPA of IT students (and thus
their academic success).
Multiple linear regression analysis was used to predict tertiary education GPA based on the ve trait
emotional intelligence factors (including global trait). A signicant regression equation was found [F (5,
282) = 4.33, p = .001], with an R2 of .071. It was found that well-being trait [β = -.03, p = .001], self-control
trait [β = -.02, p = .001], emotionality trait [β = -.02, p = .022], and global trait emotional intelligence [β =
.10, p = .001] signicantly predicted tertiary education GPA.
Multiple linear regression analysis was also used to predict tertiary education GPA based on the
eight multiple intelligence factors. A signicant regression equation was found [F (8, 279) = 6.25, p < .001],
with an R2 of .152. It was found that bodily/kinesthetic [β = -.01, p = .020], logical/mathematical [β = .02,
p < .001], visual/spatial [β = -.01, p < .001], intrapersonal [β = .01, p = .031], and naturalist intelligence [β
= .01, p = .031] signicantly predicted tertiary education GPA.
Discussions
The paper analyzed relations between trait emotional intelligence factors, multiple intelligences
prole and the IT student academic success. The examination of hypotheses that were formulated
following dened aims and goals was performed by empirical research on a sample of 288 university IT
students from Serbia and Greece.
Even though secondary education GPA was identied as the most important predictor of university
academic success, there was no signicant difference detected for this factor between IT students in
Serbia and Greece. Students from Serbia did achieve signicantly higher tertiary GPA than students
from Greece, which explains higher predictor importance of country of origin. In general, increases
in secondary education GPA were correlated with increases in tertiary education GPA. These results
are consistent with Blackmore, Hird and Anderton (2021), that identied high school STEM subjects’
prociency as a signicant determinant of overall GPA, especially for engineering students. However,
there were reports that point that high school GPA was not an effective predictor of success at higher
levels of education (Noble and Sawyer, 2002). Even though female students reported signicantly higher
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Aleksić, V., & Politis, P. (2023). Trait Emotional Intelligence and Multiple Intelligences as Predictors of Academic Success in
Serbian and Greek IT Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(2), 173-185.
secondary education GPA than males, gender difference was not identied as signicant predictor of
university academic success. These ndings are not consistent with (Tessema, Ready and Malone,
2012), who reported a statistically signicant moderate effect of gender on students’ GPA.
The type of living environment (e.g., urban or rural) was not identied as a signicant predictor of
academic success, which is consistent with (Khan et al., 2012; Kurek and Górowski, 2020).
Students from Greece reported signicantly higher English prociency level than students from
Serbia. English prociency level was a signicant predictor of academic performance. These ndings are
consistent with (Geide-Stevenson, 2018; Martirosyan, Hwang and Wanjohi, 2015).
The validity of Serbian and Greek translated and adapted 30-item versions of TEIQue-SF (Petrides,
2009) was conrmed. These ndings are consistent with the results of referent research (Jolić-Marjanović
and Altaras-Dimitrijević, 2014; Stamatopoulou, Galanis and Prezerakos, 2016). Students from Serbia
achieved signicantly higher scores than students from Greece in all trait emotional intelligence factors.
There were signicant gender differences in trait emotional intelligence factor scores, and female students
achieved signicantly higher scores in well-being and emotionality trait emotional intelligence. These
ndings are consistent with (Perera, 2015; Petrides and Furnham, 2000; Petrides and Mavroveli, 2018).
There was no signicant correlation between trait emotional intelligence and secondary education GPA,
which is consistent with (Shipley, Jackson and Segrest, 2010). Contrary to (Herrera Torres, Buitrago
Bonilla and Cepero Espinosa, 2017) ndings, which reported signicantly lower emotional intelligence
scores in students living in rural areas, our research did not identify signicant differences in the type of
living environment. Increases in the prociency level of English were correlated with decreases in well-
being trait, emotionality trait, and global trait emotional intelligence scores. These ndings are contrary
to Dewaele (2018) who reported that English prociency level positively correlated with emotionality and
global trait emotional intelligence levels.
The educational data mining procedure using predictive neural network model conrmed that
emotionality, sociability, self-control, and global trait emotional intelligence factors were signicant
predictors of tertiary academic success. This can be explained from two aspects. First, the structure
of the university IT study programmes is very time and cognitive demanding, having a large number of
electable subjects correlated to various areas of IT application, so students are often separated into small
groups and directed towards self-regulated learning. Rode et al. (2007) stated that students with higher
levels of emotional intelligence are more efcient at upholding the energy needed for high cognitive
performance over longer periods of time, and redirecting negative emotions into productive behaviors.
Second, university IT students are exposed to higher level of stress due to a large number of various
tasks that are often related to the eld of technological application and as such require advanced levels
of digital skills and competence. In addition, a signicant number of lecturers on contemporary IT courses
are engaged from the IT industry, and consequently often use questionable teaching methods. Students
with higher level of emotional intelligence are associated with lower level of acute and chronic stress
(Singh and Sharma, 2012). It should be noted that several researchers reported no signicant correlation
between emotional intelligence and tertiary academic achievement (Wurf and Croft-Piggin, 2015).
Having in mind the presented ndings, it can be concluded that hypothesis H1: The identied trait
emotional intelligence factors are the predictors of IT students’ academic success is conrmed.
The expected validity of the 119-item version of IPVIS is based on the results of referent research
(Aleksić and Ivanović, 2016) that evaluated the instrument and concluded that it was valid and reasonably
reliable. Female students achieved signicantly higher scores in naturalist intelligence, which is consistent
with (Aleksić and Ivanović, 2017). Students from Greece achieved signicantly higher scores in musical/
rhythmic intelligence. This nding can be explained by the strong socio-cultural inuence of music on
Greek society which is still permeated with vivid remnants of linguistic, cultural, architectural, and musical
spheres of civilization that have ourished from the second millennium BC until the rst millennium AD
(Charidimou et al., 2022). Students from Serbia exceeded in intrapersonal and naturalist intelligence
scores. Until the mid-twentieth century, Serbia remained a country of peasant smallholders starting from
the Ottoman conquest in second half of the 14
th
and the rst half of the 15
th
century (Šljukić, 2006). Most
of the Serbian population lived simple lives, focused on themselves, recognizing own abilities, capacities,
intuition and recognizing patterns in nature. This behavior patterns inevitably inuenced the prole of the
people who inhabited it, and obviously can still be recognized. Students living in rural areas achieved
signicantly higher scores in naturalist intelligence. Predictive neural network model conrmed that all
multiple intelligence factors were signicant predictors of tertiary academic success. Logical/mathematical
intelligence was identied as the most important predictor, following intrapersonal intelligence, naturalist
intelligence, etc., which is consistent with (Torreon and Sumayang, 2021) ndings. This was expected, as
university IT education relies heavily on procient knowledge and skills in applied mathematics and logic,
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182
Aleksić, V., & Politis, P. (2023). Trait Emotional Intelligence and Multiple Intelligences as Predictors of Academic Success in
Serbian and Greek IT Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(2), 173-185.
that can be observed in programming which is a foundation of every contemporary IT curriculum.
Having in mind the presented ndings, it can be concluded that hypothesis H2: The identied
multiple intelligences prole is the predictor of IT students’ academic success is conrmed.
Limitations
The research was realized with certain limitations. Even the sample was adequate in structure and
size, and the psychometric instruments were reliable and valid, the conclusions about identied causal
relationship between sociodemographic factors, trait emotional intelligence, multiple intelligences prole,
and academic success were impossible to conrm due to the correlation nature of the research, so the
focus of these relations should be claried by longitudinal research which would add dynamic dimension.
Conclusions
With the rapid implementation of various digital educational platforms and services which gained
additional importance during the Covid-19 emergency, an extremely large amount of new data on student
activities is generated daily. This big data represents an exceptional pool from which various predictions
of student behavior and performance can be derived. The present study examined the effects of trait
emotional intelligence and multiple intelligences prole of IT students on their academic success at two
universities from Serbia and Greece.
Secondary education GPA and logical/mathematical intelligence were the two most signicant
predictors of university IT students’ academic success. In addition to the identied trait emotional
intelligence and multiple intelligences prole predictors, further effects are relevant to discuss. Trait
emotional intelligence factors related to multiple intelligences prole in a very complex manner. This was
expected as both concepts expand the model of general intelligence (Spearman, 1961) by including often
similar characteristics such as individual differences, academic intelligence, personality, interests, etc.
These ndings are consistent with (Keshavarz, Farahan and Khajehpour, 2014) but contrary to (Bay and
Lim, 2006) ndings that reported a signicant number of negative correlations. In general, the developed
educational data mining model proved its efciency. Over 44% of the variance in the IT student tertiary
education GPA can be explained. The extent to which the effects of trait emotional intelligence factors
and multiple intelligences prole were predictive is remarkable. It should be emphasized that the effects
of some factors were controlled by more or less typical predictors, such as prior schooling (secondary
education GPA) and English prociency level.
In the context of academic success, trait emotional intelligence and multiple intelligences prole
are especially powerful as they can help university teachers understand IT education holistically. The
sphere of IT disciplines and applications is constantly evolving and growing. Traditional software industry
is nowadays under the onslaught of machine learning, articial intelligence, blockchain and metaverse
applications, and many new technologies are on the horizon. As more and more jobs in the IT industry are
automated and taken over by computers, education stakeholders and university teachers clearly should
pay more attention to their timely assessment and adaptation of teaching practice to create a learning
environment that will empower students with appropriate current knowledge and skills, such as critical
thinking, complex problem solving, design thinking, cognitive exibility, business analytics, etc. Many of
the listed abstract skills students can master much more efciently if the content and teaching methods
are adjusted to their individual characteristics, which can be observed via their trait emotional intelligence
and multiple intelligence prole.
Future research will include experimental measurement points to better reect the complex spectrum
of academic performance and success. Due to the sample size and structure, student performance in
various courses should be separately analyzed. Regardless of stated limitations, this research supports
the importance of fostering a student-centered learning model through tertiary education, with special
emphasis on taking into account students’ personal dispositions and traits.
Conict of interests
The authors declare no conict of interest.
Acknowledgements
The authors would like to thank the respondents who participated in the research and the reviewers
who made a valuable contribution to the quality of the work by giving constructive suggestions.
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183
Aleksić, V., & Politis, P. (2023). Trait Emotional Intelligence and Multiple Intelligences as Predictors of Academic Success in
Serbian and Greek IT Students, International Journal of Cognitive Research in Science, Engineering and Education (IJCRSEE),
11(2), 173-185.
Author Contributions
Conceptualization, V.A. and D.P.; Resources, V.A. and D.P.; Methodology, V.A.; Investigation, V.A.
and D.P.; Data curation, V.A.; Formal Analysis, V.A. and D.P.; Writing – original draft, V.A. and D.P.; Writing
– review & editing, V.A. All authors have read and agreed to the published version of the manuscript.
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