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УПРАВЛЕНИЕ И ОБРАЗОВАНИЕ
MANAGEMENT AND EDUCATION
TOM XIV (3) 2018
VOL. XIV (3) 2018
MUSICAL AND LINGUISTIC INTELLIGENCES: ARE THERE DIFFERENCES BETWEEN
SERBIAN AND GREEK STUDENTS?
Veljko Aleksić, Dionysios Politis
МУЗИКАЛНА И ЛИНГВИСТИЧНА ИНТЕЛИГЕНТНОСТ: СЪЩЕСТВУВА ЛИ РАЗ-
ЛИКА МЕЖДУ СРЪБСКИТЕ И ГРЪЦКИ СТУДЕНТИ?
Велико Алексич, Дионисос Политис
ABSTRACT: The paper presents an alternate approach to assessing student intelligence indirectly creating
the tool for comparing the efficacy of various educational systems. The authors based its research on the theory
of multiple intelligences for which profile assessment they used the instrument that was previously developed and
psychometrically proven as reliable and valid. The presumption that average musical and linguistic intelligence
significantly defers between Serbian and Greek university students was not confirmed. There are various theo-
ries that could explain this finding, but more extensive research is needed in order to confirm any of them.
Key words: Multiple intelligences, Assessment, Students.
Introduction
When educational researcher thinks of the
way to measure, compare or evaluate characteris-
tics and quality of educational systems between
countries and states, probably the first thing in
mind comes the Programme for International
Student Assessment (i.e. PISA) (OECD, 2016).
However, there is also a variety of non-
standardized assessment tools that could be used
for these purposes. In this paper, we will focus
on the idea to alternately use multiple intelli-
gences theory (Gardner, 1993) which inspired
enthusiasm in many educational circles in the
last two decades. The theory is based on the de-
scriptions of recognizable behavior, i.e. directly
visible manifestations that are also visible to the
respondent, so in turn it enables the possibility of
self-assessment.
The theory of multiple intelligences makes
it easier to understand the way students think,
enable better exploitation of their potential in
order to increase learning efficiency, understand-
ing and respecting individual differences among
students, but can also serve as a diagnostic tool
for directing further education (Booth and O'Bri-
en, 2008).
Musical and Linguistic Intelligences
The theory of multiple intelligences defines
eight aspects of human intelligence: Linguistic,
Mathematic, Visual, Kinesthetic, Musical, Natu-
ral, Interpersonal and Intrapersonal. Practice has
shown that students who are considered intelli-
gent in one particular area may be undermined in
other. Each person has different capacities in all
aspects of intelligence (Armstrong, 2009). Ac-
cording to the theory, people achieve every intel-
lectual capacity in certain degree, but which will
be more developed most often depends on genet-
ic predisposition and socio-cultural conditions.
There are various ways for one intelligence to be
more developed because it is not possible to
standardize the set of attributes that an individual
must possess to be considered intelligent in a
particular category. The theory emphasizes a
multitude of ways in which people can show en-
dowment for certain intelligence or intelligences.
In accordance with it, learning can be more ef-
fective if we focus on the development of indi-
vidual instructions for these intelligences.
Students can have obvious benefits from de-
termining their intelligences profile. Namely, a
student who is more familiar with the structure
and advantages of his intellect has the opportuni-
8
ty to use appropriate techniques and learning
strategies more successfully. A combination of
instructions in accordance with the students’
multiple intelligences profile leads to improved
performance, better satisfaction and attitude to-
ward teachers (John et al., 2011).
According to the multiple intelligences theo-
ry, musical intelligence is related to the recogni-
tion and use of rhythmic and tone patterns,
recognition of sound, speech and musical in-
struments. It is used to interpret and create mu-
sic. Linguistic intelligence is based on the use of
primary mean of communication in humans. It is
reflected in symbolic thinking, use of language
and the skill of reading and writing. For the pur-
pose of the research, we first selected linguistic
intelligence having in mind vast spoken and writ-
ten language differences between Serbian and
Greek. Secondly, we selected musical intelli-
gence to test out the theory that practically all
Balkan countries share a large portion of their
musical heritage.
The profile of multiple intelligences is used to
assess students’ personal intellectual disposition.
Difficulties in measuring attitudes, skills and
characters are rooted in the complicated proce-
dure of translating these qualities into quantita-
tive measures in order to be able to analyze data
(Aleksić and Ivanović, 2016).
Research and Discussion
The main goal of the research is the self-
assessment of musical and linguistic intelligenc-
es of higher education students from Serbia and
Greece, and the analysis of their relations with
language and cultural environment characteris-
tics.
The research was conducted from June 2016
until November 2017 at two higher education
institutions:
• University of Kragujevac, Faculty of Tech-
nical Sciences Čačak, Serbia
• Aristotle University of Thessaloniki, De-
partment of Informatics, Greece
Institutions were selected so that they repre-
sent different geographical, socio-economic, lan-
guage and cultural environments. In total, 146
students aged 20 to 23 participated in the re-
search. A total of 18 questionnaires (12.3%)
were rejected as incomplete or incorrectly filled,
so the valid sample was (N = 128) students.
Both universities are located in urban areas
and the faculties at which students were surveyed
are doing teaching and research in similar scien-
tific fields. The valid sample consisted of 46.9%
students from Greece (N = 60) and 53.1% stu-
dents from Serbia (N = 68). The average age of
the students was 22.1 years (SD = 0.93). The
demographic characteristics of the valid sample
of the respondents are given in Table 1.
Table 1. Demographic characteristics
Gender Age Country
Total
N
Percent
Male
20
GR
5
3.9%
SRB
3
2.3%
21
GR
7
5.5%
SRB
6
4.7%
22
GR
14
10.9%
SRB
20
15.6%
23
GR
15
11.7%
SRB
15
11.7%
Subtotal
85
66.4%
Female
20
GR
2
1.6%
SRB
1
0.8%
21
GR
0
0%
SRB
6
4.7%
22
GR
6
4.7%
SRB
12
9.4%
23
GR
11
8.6%
SRB
5
3.9%
Subtotal
43
33.6%
It was assumed that self-evaluation of student
strengths and weaknesses reflect general and ac-
ademic personal concepts. For the purpose of the
research, the part of IPVIS (Aleksić and
Ivanović, 2016) multiple intelligences profile
assessment psychometric instrument was used.
Students anonymously, voluntarily and inde-
pendently filled out a questionnaire in paper-
pencil and electronic forms in school premises.
The role of the examiners was to give the initial
instructions and ensure the testing procedure in
accordance with the standards in the field of psy-
chological and pedagogical research (Carretero-
Dios and Pérez, 2007).
1. Item analysis and descriptive statistics
Descriptive indicators and the normality dis-
tribution indicators of the linguistic and musical
intelligence scales are shown in Table 2.
9
Table 2. Item analysis
Scale
Item
M
SD
Skew.
Kurt.
Linguistic intelligence
LI1
2,96
1,354
-,180
-,256
LI2
3,20
1,381
-,230
-,404
LI3
3,14
1,253
-,612
,056
LI4
3,20
1,328
-,407
-,446
LI5
3,13
1,129
-,483
-,088
LI6
2,14
1,332
,793
,441
LI7
3,09
1,389
-,618
-,544
LI8
3,16
1,303
-,418
-,367
LI9
2,63
1,345
,107
-,745
LI10
2,64
1,332
-,044
-,750
LI11
3,00
1,409
-,292
-,959
LI12
3,40
1,388
-,657
-,549
LI13
2,77
1,395
,039
-,888
Musical intelligence
MI1
1,34
1,513
1,350
1,425
MI2
2,18
1,619
,529
-,752
MI3
2,65
1,477
,136
-,981
MI4
2,99
1,471
-,016
-1,236
MI5
2,68
1,500
,080
-1,239
MI6
2,90
1,385
-,231
-,555
MI7
3,65
1,271
-,479
-,523
MI8
3,05
1,427
-,395
-1,022
MI9
3,00
1,280
-,046
-,955
MI10
3,06
1,586
,100
-1,131
By analyzing the results of the answer distri-
bution, it was found that the values of the skew-
ness and kurtosis indices for all items are within
limits of ±2 which is satisfactory (Gravetter and
Wallnau, 2014). Resulting frequency allocation
histograms are shown in Figure 1.
Fig. 1. Resulting frequency histograms for the
Linguistic and Musical intelligence
Visual inspection of the distribution and val-
ues of the curvature indicators shows that the
results on both scales are negatively curved, that
is, that most respondents achieved better results.
An independent samples t-test was conducted
to examine whether there was a significant gen-
der difference between students in relation to
their self-estimation of the linguistic intelligence.
The test did not reveal a statistically significant
difference between male and female students (t =
-2.73, df = 126, p = .007). However, the test re-
vealed a statistically significant difference be-
tween male and female students in estimated mu-
sical intelligence (t = -3.27, df = 110.9, p = .001).
Males (M = 53.8, SD = 16.3) reported signifi-
cantly lower levels of musical intelligence than
did females (M = 62.0, SD = 11.7), as shown in
Figure 2.
Fig. 2. Gender related difference in estimated
levels of Musical intelligence
When we conducted the independent samples
t-test to examine whether there was a difference
in estimated intelligences between students from
Serbia and Greece, no significant result was re-
vealed (t = 1.95, df = 126, p = .054) (t = 0.89, df
= 126, p = .375) for neither linguistic nor musical
intelligence, respectfully.
2. Factor analysis and reliability
The applicability of factor analysis was ini-
tially checked by Kaiser-Meyer-Olkin Measure
of Sampling Adequacy and Bartlett’s test of
sphericity. Since the KMO index value (0.760)
was satisfactory and the result of the Bartlett’s
test (χ2 (253) = 958.9; p < 0.001) was statistically
significant, it was confirmed that factor analysis
could be carried out and the sample is appropri-
ate.
The isolation of factors in exploratory factor
analysis was done using the maximum likelihood
method with varimax rotation. A single-factor
10
solution had a satisfactory value (2.57) which
explained 11.2% of the response variance.
The values of the Cronbach’s Alpha coeffi-
cients were (.80) and (.76) for the linguistic and
musical intelligences respectfully, which is satis-
factory.
The result of a single-factor variance analysis
(ANOVA) again indicated that there were no
significant differences in assessed intelligence
levels between Greek and Serbian students.
Conclusion
The initial idea that language differences be-
tween nations could influence the level of lin-
guistic intelligence of their students basically
failed. This could be a consequence of various
reasons. For instance, we (maybe unfortunately)
choose two countries that have very close eco-
nomic, historical and geographical ties, cultural
similarity, same main religion etc. Further re-
search on this finding could be sought by extend-
ing a sample at some country which language is
more or less “exotic” to the region of Southeast
Europe, such as Iran, or even China or Japan that
use logograms instead of letters. On the other
side, the assessed levels of musical intelligence
were expected.
As mentioned at the introduction, PISA re-
sults could and should be used as a valid refer-
ence list for the quality scoreboard of educational
systems through the world, but maybe with the
novel approach that is presented in this paper we
could see more in it than meets the eye.
References
1. Aleksić, V., and Ivanović, M. 2016. “Psy-
chometric evaluation of the reliability of IPVIS-
OS multiple intelligences assessment instrument
for early adolescents,” Journal of Educational
Sciences and Psychology, VI(LXVIII) 1, pp. 21-
34.
2. Armstrong, T. 2009. Multiple Intelligence
in the Classroom (3rd ed.). Alexandria, VA, US:
Association for Supervision and Curriculum De-
velopment.
3. Booth, R., and O’Brien, P. 2008. “An ho-
listic approach for counsellors: Embracing mul-
tiple intelligences,” International Journal for the
Advancement of Counselling, 3(2), pp. 79–92.
4. Carretero-Dios, H., and Pérez, C. 2007.
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instrumental studies: Considerations about test
selection in psychological research,” Interna-
tional Journal of Clinical and Health Psycholo-
gy, 7 (3), pp. 863–882.
5. Gardner, H. 1993. “Multiple intelligences:
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31 (02), pp. 31–1214–31–1214.
6. Gravetter, F., and Wallnau, L. 2014. Essen-
tials of statistics for the behavioral sciences (8th
ed.). Belmont, CA, US: Wadsworth.
7. John, C., Rajalakshmi, M., and Suresh, K.
2011. “Fostering study skills, attitudes and habits
among students using the multiple intelligence
approach,” Language in India, 11, pp. 92–109.
8. OECD. 2016. PISA 2015 Results (Volume
I): Excellence and Equity in Education. Paris,
France: OECD Publishing.
Veljko Aleksić
University of Kragujevac
Faculty of Technical Sciences Čačak
Svetog Save 65, 32000 Čačak, Serbia
e-mail: veljko.aleksic@ftn.kg.ac.rs
Dionysios Politis
Aristotle University of Thessaloniki
Department of Informatics
Thessaloniki 541 24, Greece
e-mail: dpolitis@csd.auth.gr
