Creative Education
2013. Vol.4, No.9, 605-610
Published Online September 2013 in SciRes (http://www.scirp.org/journal/ce) http://dx.doi.org/10.4236/ce.2013.49086
Copyright © 2013 SciRe s . 605
Multiple Intelligence Theory Can Help Promote Inclusive
Education for Children with Intellectual Disabilities and
Developmental Disorders: Historical Reviews of Intelligence
Theory, Measurement Methods, and Suggestions for Inclusive
Education
Junichi Takahashi
Department of Developmental Disorders, National Institute of Mental Health, National Center of Neurology and
Psychiatry, Toky o, Ja pan
Received July 5th, 2013; revised August 5th, 2013; accepted August 12th, 2013
Copyright © 2013 Junichi Takahashi. This is an open access article distributed under the Creative Commons At-
tribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the
original work is properly cited.
Inclusive education, based on the principle that all children (including those with disabilities) should re-
ceive similar education, has been recently adopted in primary and secondary schools throughout several
countries. Within an inclusive education context, teachers are faced with the challenge of developing their
knowledge and skills necessary to properly assess the intellectual abilities of a wide range of children.
Although intelligence has been examined for over 100 years, researchers are still debating what abilities
should or should not be classified as belonging to the domain of intelligence. In order to effectively apply
intelligence theory and assessment methods for inclusive education, we compared traditional intelligence
theory (Spearman’s two-factor model) with a more recent intelligence theory (Gardner’s multiple intelli-
gence theory). Spearman’s theory focuses on elementary perceptual processes by using the single g factor,
whereas Gardner’s theory recognizes several types of intelligence. On the basis of these reviews, we pro-
pose the utility of multiple intelligence theory for inclusive education, considering the various profiles of
intelligence shown by children with intellectual disabilities and developmental disorders.
Keywords: Multiple Intelligence Theory; Inclusive Education; Intellectual Disabilities; Developmental
Disorders
Introduction
In Japan, the educational system of compulsory schools for
children who need special education began in 1979. All chil-
dren who had various forms of disability could receive school
education. Attending special support school means that these
children receive compulsory education. Thus, the number of chil-
dren who attended special support schools continued to increase.
According to a report by MEXT (Ministry of Education, Cul-
ture, Sports, Science, and Technology) in 2008, 0.6% (60,302
children) of all children (primary and secondary schools) at-
tended a special support school, and 1.2% (124,166 children) of
all children attended a special support class within a general
school.
In order to support children’s education, special education
teachers need to assess whether or not special education is
needed for certain children. Thus, it is necessary to precisely
measure children’s intellectual abilities (Koegel, Matos-Freden,
Lang, & Koegel, 2012; Pivik, McComas, & Laflamme, 2002).
Special education teachers need to acquire the knowledge and
skills required for proper measurement of children’s intelli-
gence (i.e., IQ: intelligence quotient) in order to adapt their
teaching to children with disabilities (i.e., intellectual disabili-
ties, autism spectrum disorder, attention deficit hyperactivity
disorder, dyslexia, and learning disabilities).
Recently, inclusive education is being seen more broadly as a
reform of the educational system around the world (UNESCO,
2001). Children who need special education, in addition to
typically developing children, should be educated together in a
general classroom. Further, in Japan, part of the School Educa-
tion Act was revised in 2007 with the intention that the educa-
tional system supports each child’s special needs. Here, educa-
tion for children with disabilities extends to general society in
addition to special support schools. Efforts of teachers, along
with the government, school boards, and principals, are needed
to facilitate inclusive education. In this situation, teachers have
to assess children’s intelligence in order to adapt their teaching
to all children, including children with disabilities (Pivik et al.,
2002). General education teachers need to conduct measure-
ments of children’s behavior and develop intervention plans
based on each child’s symptoms (Koegel et al., 2012). However,
general education teachers might have difficulty working with
children with disabilities given their lack of experience in deal-
ing with this student population (Koegel et al., 2012; Robertson,

J. TAKAHASHI
Copyright © 2013 SciRe s . 606
Chamberlain, & Kasari, 2003; Takahashi, in Press).
However, researchers are still debating what sorts of abilities
should and should not be classified as part of intelligence (e.g.,
Gottfredson, 2004). In this paper, first, we review traditional
intelligence theory and assessment methods and discuss their
theoretical problems with regard to teaching methods for chil-
dren with disabilities. Second, we review more recent intelli-
gence theory, such as multiple intelligence theory and its as-
sessment methods. On the basis of these reviews, we suggest
that multiple intelligence theory could be applied to inclusive
education by focusing on disabled children’s uneven intellec-
tual profiles.
Traditional Intelligence Theories and
Assessments
Intelligence has been studied for about 100 years, in which
researchers have examined intelligence assessment methods
and its theory. Considering the assessment methods of intelli-
gence, two types of intelligence measures were proposed: one
aimed to assess elementary perceptual processes (e.g., Cattell,
1890; Galton, 1883; Spearman, 1904), and the other focused on
an assessment of complex cognitive processes (e.g., Binet-
Simon, 1905; Wechsler, 1997). The validity of these findings
has yet to be fully discussed according to previous studies from
Spearman (1904) and Binet and Simon (1905).
Intellectual assessment methods were first derived from Gal-
ton’s measurement of elementary perceptual processes. Galton
measured auditory and visual sensory discrimination abilities,
reaction times to stimuli, and hand squeeze pressure perform-
ance on a dynamometer. Galton believed that sensations were
the foundation for complex cognitive functions. He assumed
that people with high in intelligence could effectively discrimi-
nate stimuli compared to people with low in intelligence (Gal-
ton, 1883). Cattell (1890) expanded Galton’s intelligence as-
sessments and proposed ten psychological components, such as
tactile discrimination, thresholds for pain, weight discrimina-
tion, and reaction times to auditory stimuli.
In contrast to these scales, Binet focused on complex mental
processes. In 1905, Binet and Simon published the “measuring
scale of intelligence” to assess children who needed special
education in Paris (Binet & Simon, 1905). This measurement
includes 30 subtests, such as naming, semantic judgments,
memory, reasoning, and digit span. In each subtest, examina-
tion items were arranged on the basis of difficulty. In 1908,
they revised the scale by grouping tests by age level (e.g., for
the digit span subtest, longer spans were intended for older
children). In the revised version of Binet-Simon’s scale, as-
sessment began at the child’s age level and proceeded to a
higher or lower level based on performance criteria. According
to this method, mental age (the highest age level) was defined.
The Binet-Simon scales have influenced later intellectual as-
sessment methods (Boake, 2002). In the 1930’s, Wechsler fo-
cused on a wide range of intellectual abilities and a broadband
intelligence test for adults because he considered intelligence to
be guided by several personality aspects. In contrast to the early
emphasis on the assessment of elementary perceptual abilities
(e.g., Spearman, 1904), Wechsler suggested that intelligence
should be assessed in relation to a person’s overall ability,
similar to Binet. The Wechsler intelligence scales are classi-
fied as the WAIS (Wechsler Adult Intelligence Scale: Wechs-
ler, 1997), WISC (Wechsler Intelligence Scale for Children:
Wechsler, 1991), and WPPSI (Wechsler Preschool and Primary
Scale of Intelligence: Wechsler, 1999) for adults, school-aged
children, and preschoolers, respectively. These scal es c an meas-
ure specific aspects of ability through various subtests; these
subtests yield both a verbal and performance IQ (VIQ and PIQ,
respectively), in addition to a full scale IQ (FSIQ). For example,
the WAIS-III (Wechsler, 1997) is composed of seven subtests
of verbal abilities (Vocabulary, Similarities, Information, Com-
prehension, Arithmetic, Digit Span, and Letter-Number Se-
quencing) and seven subtests of performance abilities (Picture
Arrangement, Picture Completion, Block Design, Matrix Rea-
soning, Digit-symbol Coding, Symbol Search, and Object As-
sembly). The Wechsler intelligence scales have been recom-
mended as a crosscheck for neuropsychological functions among
children wit h d isa bilit ies (e.g ., K aufman, Lo ng, & O ’Neal, 1986 ).
In contrast to the aforementioned intelligence tests, the study
of intellectual testing theory has not advanced much. Theoreti-
cal considerations have been discussed by Spearman (1904). He
obtained a significant positive correlation between sensory-
discrimination ability and children’s academic performance.
For instance, children with a large capacity for sensory-dis-
crimination tend to show higher academic performance. Thus,
he concluded that sensory-discrimination ability shares a simi-
lar capacity with academic performance among children. On
the basis of these accounts, Spearman developed an intelligence
theory based on the assumption that sensory-discrimination
capacity might coincide with intelligence, which was labeled as
a single g factor for general intelligence. He also proposed the
specific source of variance (s); these proportions have been
referred to as the “two-factor model” of intelligence (Figure 1(a)).
Problems of Traditional Intelligence Theory in
Consideration of Children with Developmental
Disorders
Traditional intelligence theory lacked innovation in terms of
developmental and cognitive approaches to intelligence (e.g.,
Sternberg & Kaufman, 1996). In addition, traditional intelli-
gence theory did not have much ecological validity (Almeida,
Prieto, Ferreira, Bermejo, Ferrando, & Ferrandiz, 2010). As
Almeida et al. (2010) indicated, since traditional intelligence
theory assessed students’ maximal performance in situations
related to school settings, there was little focus on the most
valuable aspects of cognition within real life settings. Moreover,
traditional theorists assessed students’ performance in an overly
abstract manner without consideration of cultural context and
differences between social groups. Sternberg (1997) also ar-
gued that most conventional concepts of intelligence are too
narrow and deal only with a small portion of intelligence as a
whole. Sternberg (1985) proposed three basic concepts (the
ability to adapt to one’s environment, the ability to deal with
novel tasks, and the ability to develop expertise) and suggested
that intelligence depends on acquiring these information-pro-
cessing skills and strategies.
For children with developmental disorders, the problems of
traditional intelligence theory are more evident. The two-factor
model cannot account for children with developmental disor-
ders. Children with developmental disorders, such as high func-
tioning autism spectrum disorder, attention deficit hyperactivity
disorder, and learning disorders, generally display normal IQ

J. TAKAHASHI
Copyright © 2013 SciRe s . 607
levels (i.e., IQ more than 70). However, these children show an
uneven profile among intellectual subtests (e.g., Lincoln, Cour-
chesne, Kilman, Elmasian, & Allen, 1988; Siegel, Minshew, &
Goldstein, 1996; Szatmari, Tuff, Finlayson, & Bartolucci, 1990).
For example, when assessing Wechsler Intelligence Scale per-
formance, the intelligence profile of children with autism spec-
trum disorders is characterized by VIQ (verbal IQ) < PIQ (per-
formance IQ) with the lowest subtest performance on the com-
prehension subtest and highest performance on the Block De-
sign subtest. However, these children’s FSIQ (full scale IQ) is
over 70, indicating a normal IQ level. The two-factor model
might not correctly assess abilities for these children given that
the model relies more on the single g factor and does not ac-
count for the uneven profiles in subtest performance among
children with autism spectrum disorders. Similar arguments
against a single g factor for assessing children with develop-
mental disorders have been proposed elsewhere (e.g., Anderson,
1998).
In addition, current assessment methods only include deter-
minate aspects of children’s abilities. For example, although the
Wechsler Intelligence Scales can measure verbal or logical abi-
lities through traditional paper-and-pencil tests, people should
be measured on abilities that are more relevant to their every-
day environment. Although IQ scores for children with devel-
opmental disorders are generally lower than those of typically
developing children when using the Wechsler Intelligence Scale
(e.g., Happe, 1994), in terms of performance on specific tasks,
such as visual search or memory, performance of children with
developmental disorders is as good or better than that of typi-
cally developing children (e.g., Dawson, Soulieres, Gernsba-
cher, & Mottron, 2007). From perspective, traditional intelli-
gence theory and its assessment would be associated with de-
terminate aspects of intelligence, which would not be ecologi-
cally val i d.
More recently, Gardner (1983) proposed a new approach for
the conceptualization of intelligence, which he termed multiple
intelligence (MI) theory (Figure 1(b)). Gardner (1998) argues
for various types of intelligence (at least eight independent
intelligences). Although children have the capacity to use these
eight forms, most children show different profiles in how they
blend intelligences to solve problems. Thus, MI theory could
potentially account for an uneven Wechsler intelligence profile
among children with developmental disorders. Below, we re-
view Gardner’s MI theory in more detail.
Multiple Intelligence Theory
The MI theory (Gardner, 1983, 1993, 1998) adds a nuanced
view to intelligence theory. Gardner defined intelligence as the
ability to solve problems or create products in one or more
cultural settings. He proposed that there are at least eight inde-
pendent intelligences: verbal-linguistic, logical-mathematical,
spatial, bodily-kinesthetic, musical, interpersonal, intrapersonal,
and naturalist (Gardner, 1998). For example, musical intelli-
gence is the ability to produc e a nd appreciate rhythm, pitch, and
timbre, while spatial intelligence refers to the ability to men-
tally represent and manipulate objects, navigation, mechanics,
sculpture, and geometry. Based on several domains of study
(e.g., psychology, case studies, anthropology, cultural research,
and biology), the eight intelligences are classified on the basis
of 1) potential independence with neuropsychology; 2) the ex-
istence of a “genius” in each intelligence; 3) a specific applica-
(a)
(b)
Figure 1.
The schematic diagram of two-factor model (a) and MI model (b).
(a) Spearman assumed that sensory-discrimination capacity might
be associated with intelligence. He labeled it as a single g factor
(general intelligence). He also assumed the specific source of
variance (s), for example, verbal comprehension, processing speed, or
perceptual organization; (b) Gardner proposed at least eight inde-
pendent intelligences in MI theory.
tion; 4) differences in developmental processes between people
with high and low abilities in each intelligence; 5) scientific
validity related to evolution; 6) agreement with psychophysical
findings; 7) agreement with psychometric findings; and 8) an
encoding system. The MI theory uses the concept of modules.
Modules are neural structures that process particular content. In
the case of visual processing modules, color, shape, or face
stimuli might be processed specifically. People have the capac-
ity for all eight intelligences and show uneven profiles by
blending some intelligence to adapt to the environment (Gard-
ner & Hatch, 1989). For instance, a therapist would have high
verbal-linguistic ability and sensitivity to the sounds and con-
struction of language. Surgeons would have high visual acuity
and spatial intelligence in order to manipulate the scalpel and
bodily-kinesthetic dexterity in order to properly use the tool.
Scientists require verbal-linguistic and logical-mathematic in-
telligence in order to indicate and explain new findings. More-
over, scientists also should have high interpersonal intelligence
to better interact with colleagues and be a part of a smoothly
functioning laboratory.
Emotional intelligence (EI) theory has also been proposed as
another facet of intelligence (Salovey & Mayer, 1990). Since
“emotional thoughts” (Leeper, 1948) are part of “logical thoughts,”
which contribute to general intelligence, there was little re-
search on emotional intelligence for nearly forty years (Derksen,
Kramer, & Katzko, 2002). When Gardner (1983) proposed MI
theory, researchers began to revisit EI theory. EI theory is de-
fined as the capacity to process emotional information accu-
rately and efficiently, including information relevant to the
recognition, construction, and regulation of emotion within
oneself and others (Salovey & Mayer, 1990). In order to assess
EI, three main tests have been developed: the Mayer-Salovey-
Caruso Emotional Intelligence Test (MSCEIT: Mayer, Salovey,
& Caruso, 2002), Emotional Quotient Inventory (EQ-I: Bar-On,
1997), and Self-Report EI Test (SREIT: Schutte, Malouff, Hall,
Haggerty, Cooper, Golden et al., 1998). MI theory is different
from EI theory in that the former focuses on the importance of
cognitive functioning rather than emotional functioning within
the domain of intelligence.
Although Gardner provides theoretical consideration for MI
theory, he details few assessment methods for measuring these

J. TAKAHASHI
Copyright © 2013 SciRe s . 608
facets of intelligence. Almeida et al. (2010) examined how tra-
ditional intelligence tests and those based on MI theory are
interrelated. They used the Battery of Differential and General
Aptitudes (BADyG: Yuste, Martinez Arias, & Galve, 1998) as
an assessment method. The BADyG is composed the BADy G-I
(designed for respondents aged four to six years) and the
BADyG-E1 (six to eight years). The BADyG-I is composed of
six subtests: numerical quantitative concepts, information, fig-
ural vocabulary, non-verbal mental ability, reasoning with fig-
ures, and puzzles. The BADyG-E1 is composed of eight sub-
tests: logical reasoning, analogical relationships, numerical pro-
blems, logical matrices, numerical calculus, complex verbal
orders, rotated figures, and discrimination of differences. Al-
meida and colleagues used activities to evaluate MI proposed
by Gardner, Feldman, and Krechevsky (1998) measured on a
Likert scale. Verbal-linguistic intelligence was assessed with a
“story-telling” task in which children play with a model that has
scenery and several characters. Children are then asked to make
up a story and tell it to the administrator. In addition, this facet
of intelligence was assessed with a “reporter” task in which
children were asked to tell what happened in a video after being
shown a short, voiceless video. Logical-mathematical intelli-
gence was assessed with the “game of the dinosaur” task, which
was a table game. Children advanced positions depending on a
score acquired with two dice: one marks the number of posi-
tions, and the other marks the direction to follow with a back-
ward movement and advanced sign. Childre n were ask ed whic h
die throw would be needed to win the game. This task meas-
ured numerical reasoning, logical reasoning, and spatial rea-
soning. Spatial intelligence was assessed with the “create a
sculpture” and “draw an animal, a person, and an imaginary
animal” tasks in which children were asked to draw an animal,
a person, and an imaginary animal after creating any figure
with clay. Bodily-kinesthetic intelligence was measured with
the “creative movement” task in which children were asked to
complete a few physical exercises and follow a clapping rhy-
thm while rowing. Later, children were also required to repre-
sent ideas using their body. For instance, the researcher said,
“imagine that you are a robot; move like it”. For musical intel-
ligence, the researcher conducted a “singing” task. Here, chil-
dren were simply asked to sing different songs. A musical
teacher evaluated musical competency skills, such as sensitivity
to pitch, rhythm, and musical ability. Based on results from a
confirmatory factorial analysis, the authors observed inde-
pendence between the BADyG and Gardner’s tasks. Moreover,
a single g factor did not provide an adequate level of structural
adjustment. These results, in terms of intellectual assessment,
support MI theory: MI theory does not acknowledge a single g
factor based on traditional intelligence theory. Moreover, this
study provides evidence that Gardner’s tasks might be one way
to adequately measure MI.
MI Theory Can Help Promote Inclusive
Education
MI theory has been adopted in many areas of education in-
cluding general education classes, special education classes,
and education for gifted children (Barton, 2000; Gardner &
Hatch, 1989; Reid & Romanoff, 1997). Gardner (1999) pointed
out that intelligence should be assessed to better understand the
teaching and learning process. He recommended that such as-
sessment is conducted in an “intelligence-fair” manner, where
is a focus on the capacity to solve problems considering various
cultural settings (Gardner & Hatch, 1989). Moreover, teachers
are required to view school education from an individual-cen-
tered perspective. However, in practice, current education mod-
els have all children learn the same material, in the same way,
and at the same pace, and a standard, static, decontextualized
in stru men t a sses ses progress. Gardner and Boi x-Mansilla (1994)
suggest that the goals of education should be to understand each
child in depth and help optimize development by matching a
child’s learning level with the appropriate teaching methods.
Children have the capacity to utilize all eight intelligences, and
most children show different profiles in how they blend these
intelligences to solve problems. Thus, uneven profiles on IQ
performance subtests shown by children with developmental
disorders might be easily explained. Children with dyslexia
generally show difficulty in discriminating sounds in language,
matching sounds to letter, combining letters to form words, and
recalling word images (Patterson, Marshall, & Coltheart, 1985;
Lachmann, Schumacher, & Van Leeuwen, 2009; Lachmann &
Van Leeuwen, 2007). In contrast, for listening comprehension,
children with dyslexia perform as well as typically developing
children (Torgesen, 1988). These findings suggest that children
with dyslexia have a narrow range of abilities within the ver-
bal-linguistic intelligence domain, whereas they are within a
normal range for logical-mathematical intelligence. Moreover,
Takahashi and Gyoba (2012) examined the effect of spatial
complexity on the capacity of working memory in persons dif-
fering along the Autism Spectrum Quotient (AQ: Baron-Cohen,
Wheelwright, Skinner, Martin, & Clubley, 2001). Spatial com-
plexity is thought to an affective variance (Takahashi, Kawachi,
& Gyoba, 2012). Thus, spatial complexity could not be as-
sessed by general intelligence scales such as Wechsler intelli-
gence scales, and it accords for the consideration of the MI
theory. As the results, the capacity of working memory for
individuals with higher tendency of autism was larger than
individuals with lower tendency of autism. In general, indi-
viduals with autism showed lower performance of linguistic
with materials compared with control group, whereas they per-
formed equivalently to or outperformed with visuo-spatial ma-
terials. We can interpret that Takahashi and Gyoba’s (2012)
results may show the uneven profiles on intelligence subtests
among children with developmental disorders. In this view, by
examining the effect of MI theory such as an affective variance
on perceptual performance, it may be revealed the various
characteristics of intelligence in children with developmental
disorders. This can provide proper assessment techniques for
teacher among children with disabilities, which may enhance
inclusive education.
Interestingly, Gardner also thought that MI theory could be
applied to the education of gifted children. Moreover, the po-
tent characteristics of this model seem to recognize intellectual
abilities related to music and art. From this perspective, there is
work that has examined musical and artistic intelligence among
children with savant syndrome. Savant syndrome refers to in-
tellectually impaired individuals who display outstanding abili-
ties within very few domains (idiot-savant: Down, 1887). Sa-
vant skills can be observed as exceptional memory or excep-
tional musical or artistic skills (e.g., Heaton & Wallace, 2004;
Howlin, Goode, Hutton, & Rutter, 2009; Mottron, Dawson, &
Soulieres, 2009; Pring, 2005). Rimland (1978) investigated the
proportion of children with savant skills who had parents with
an autism spectrum disorder (5400 parents). They showed that

J. TAKAHASHI
Copyright © 2013 SciRe s . 609
531 parents (9.8%) had savant skills. Among the children of
those parents, 53% had reported outstanding musical skills,
40% had outstanding memory, 25% had exceptional mathe-
matical skills, and 19% had exceptional artistic talent. One
thing that savant artists display is a tendency to sketch linear
perspective and ignore perceptual size constancy. Without spe-
cial training, savant artists can use complex graphic strategies,
such as linear perspective, foreshortening occlusion, and pro-
portioning (Pring, 2005). Musical savants generally show the
ability to recognize, label, and remember pitch information
without references. Pitch skill is common among savants who
do not even have musical talent (e.g., calendar calculator), sug-
gesting that this skill among musical savants might be based on
their outstanding memory ability. Mechanisms involved in
these aforementioned savant abilities are not yet fully under-
stood. One hypothesis, the Weak Central Coherence (WCC:
Frith, 1989; Happe, 1999) theory, might directly address the
underlying mechanisms (Heaton & Wallace, 2004). The WCC
hypothesis assumes that people with an autism spectrum disor-
der tend to have difficulty processing global information, such
as Gestalt or context-dependent information; thus, these people
frequently show processing biases in favor of local features.
One possible prediction follows from this theory: savant skills
are observed on tasks where good featural processing conveys
an advantage (Heaton & Wallace, 2004). As Gardner indicated
with MI theory, if musical and artistic abilities are recognized
as intelligence, children with savant syndrome can demonstrate
exceptional skills in some domains. In contrast, the two-factor
model assumes a single g factor; thus, the skills of children
with savant syndrome might not be recognized because these
children do not score well on measures of general ability. Simi-
lar points can be made for children with other developmental
disorders. As Happe (1994) showed, IQ scores (e.g., Wechsler
scales) for most children with developmental disorders are gen-
erally lower than those of typically developing children. There-
fore, when assessing intelligence with the two-factor model,
these children may be viewed as having low intellectual com-
petency. However, according to the WCC hypothesis, for spe-
cific perceptual tasks with processing biases in favor of local
features (e.g., visual search or detection tasks), these children
show similar or better performance as compared to typically
developing children (Dawson, Soulieres, Gernsbacher, & Mot-
tron, 2007). By assuming various types of intelligence, teachers
and researchers can better evaluate the abilities of children with
developmental disorders.
Reid and Romanoff (1997) applied MI theory to a gifted
education program as part of the Yale Summer Psychology
pilot project. In this project, teachers and administrators created
curricula in which students must use creative, practical, and
analytical thinking to solve actual or virtual real-world prob-
lems. One observer for every five children conducted prob-
lem-solving assessments. The observer recorded each student’s
problem solving strategies in detail. For example, the observers
administered the tangram puzzles task, which requires spatial
and logical-mathematical intelligences. The observer rated each
student’s performance by using a four-point scale (always evi-
dent, strongly evident, evident, or not evident), based on 1) use
of a logical strategy without clues; 2) incorporation clues and
new information; 3) response time for a complex problem; 4)
persistence with difficult tasks; 5) the degree of absorption in
the task; and 6) perseverance. Observers employed these crite-
ria such that students who scored within the “always evident”
or “strongly evident” category in two out of the three intelli-
gences were identified as having received adequate gifted edu-
cation services. Reid and Romanoff (1997) reported that stu-
dents were identified as having gifted performance about 17%
to 20% within 2 years in math and reading tests compared with
students who had no gifted education. These results indicate
that education on problem-solving approaches that promote
creative, practical, and analytical thinking affects the incense-
ment of school performance in addition to the development of
useful problem-solving strategies.
With these curricula, teachers provide students many oppor-
tunities to understand important concepts and topics, which
induce thinking about a problem in ways different from other
students. Moreover, by conducting these programs in regular
classrooms for all students, teachers get the opportunity for
demonstrative teaching. Actually, Reid and Romanoff (1997)
found that such opportunities encouraged classroom teachers to
develop curricula and teaching methods to better aid students’
understanding. Interestingly, the authors also indicated that
these gifted education programs could easily apply to all stu-
dents. As part of inclusive education, teachers might be re-
quired to assess all children’s abilities to understand the teach-
ing and learning process. Specifically, teachers need to under-
stand the uneven profiles on intelligence subtests among chil-
dren with developmental disorders. Some abilities might be
higher for these children as compared to typically developing
children. This creates a new educational point of view, sug-
gesting that all children, including those with and without dis-
abilities, are smart in some fashion or another (Gottfredson,
2004). However, as is the case at Japanese universities, teacher-
training programs do not provide adequate education or proper
assessment techniques for working among children with dis-
abilities. Specifically, these programs frequently impose gener-
alized assessment methods or theories based on the traditional
two-factor model. For the progress of inclusive education, tea-
cher-training programs must actively provide general education
teachers with the necessary skills and assessment techniques
that are based on MI theory.
Conclusion
In this paper, we compared traditional intelligence theory
(Spearman’s two-factor model) with a more recent intelligence
theory (Gardner’s multiple intelligence theory) to effectively
apply intelligence theory and assessment methods for inclusive
education. Considering the various profiles of intelligence
shown by children with developmental disorders and intellec-
tual disabilities, we propose the utility of multiple intelligence
theory for inclusive education because Gardner’s theory recog-
nizes several types of intelligence. These considerations may
enhance teacher-training programs in Japanese universities that
provide adequate education or proper assessment techniques for
working among children with disabilities.
REFERENCES
Almeida, L. S., Prieto, M. D., Ferreira, A. L., Bermejo, M. R., Ferrando,
M., & Ferrandiz, C. (2010). Intelligence assessment: Gardener mul-
tiple intelligence theory as an alternative. Learning and Individual
Differences, 20, 225-230. doi:10.1016/j.lindif.2009.12.010
Anderson, M. (1998). Mental retardation, general intelligence and
modularity. Learning and Individual Differences, 1 1, 249-256.
Bar-On, R. (1997). Bar-on emotional quotient inventory (EQ-i): A test

J. TAKAHASHI
Copyright © 2013 SciRe s . 610
ofemotional intelligence. Toronto: Multi-Health Systems.
Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley,
E. (2001). The autism-spectrum quotient (AQ): Evidence from as-
perger syndrome/high-functioning autism, males and females, scien-
tists and mathematicians. Journal of Autism and Developmental Dis-
orders, 31, 5-17. doi:10.1023/A:1005653411471
Barton, J. M. (2000). To include or not to include? That is not the ques-
tion. Journal of Inquiry and Practice, 3, 329-341.
Binet, A., & Simon, T. (1905). New methods for the diagnosis of the
intellectual level of subnormals. In H. H. Goddard (Ed.), Develop-
ment of intelligence in children (the Binet-Simon Scale). Baltimore:
Williams & Wilkins.
Boake, C. (2002). From the Binet-Simon to the Wechsler-Bellevue:
Tracing the history of intelligence testing. Journal of Clinical and
Experimental Neuropsychology, 24, 383-405.
doi:10.1076/jcen.24.3.383.981
Cattell, J. McK. (1890). Mental tests and measurements. Mind, 15, 373-
381. doi:10.1093/mind/os-XV.59.373
Dawson, M., Soulières, I., Gernsbacher, M. A., & Mottron, L. (2007).
The level and nature of autistic intelligence. Psychological Science,
18, 657-662. doi:10.1111/j.1467-9280.2007.01954.x
Derksen, J., Kramer, I., & Katzko, M. (2002). Does a self-report meas-
ure for emotional intelligence assess something different than general
intelligence? Personality and Individual Differences, 32, 37-48.
doi:10.1016/S0191-8869(01)00004-6
Frith, U. (1989). Autism: Explaining the enigma. Cambridge, MA: Black-
well.
Galton, F. (1883). Inquiries into human faculty and its development.
London: Macmillan. doi:10.1037/14178-000
Gardner, H., & Hatch, T. (1998). Multiple intelligences go to school:
Educational implications of the theory of multiple intelligences. Edu-
cational Researcher, 18, 4-9.
Gardner, H., & Boix-Mansilla, V. (1994). Teaching for understanding
within and acrossthe disciplines. Educational Leadership, 51 , 14-18.
Gardner, H. (1983). Frames of mind. New York: Basic Books.
Gardner, H. (1993). Creating minds: An anatomy of creativity seen
through the lives of Freud, Einstein, Picasso, Stravinsky, Eliot, Gra-
ham, and Gandhi. New York: Basic Books.
Gardner, H. (1998). A multiplicity of intelligences. Scientific American,
9, 19-23.
Gardner, H. (1999). Intelligence reframed: Multiple intelligences for
the 21st century. Ne w Y o rk: Basic Books.
Gardner, H., Feldman, D., & Krechevsky, M. (1998). Project spectrum
—Building on children’s strengths: The experience of project spec-
trum. New York: Teachers College Press.
Gottfredson, L. S. (2004). Schools and the g factor. The Wilson Quar-
terly, Summer, 35-45.
Happe, F. G. E. (1994). Wechsler IQ profile and theory of mind in Au-
tism: A research note. Journal of Child Psychology and Psychiatry,
35, 1461-1471. doi:10.1111/j.1469-7610.1994.tb01287.x
Heaton, P., & Wallace, G. L. (2004). Annotation: The savant syndrome.
Journal of Child Psyc h o l o g y a nd Psychiatry, 45, 899-911.
doi:10.1111/j.1469-7610.2004.t01-1-00284.x
Howlin, P., Goode, S., Hutton, J., & Rutter, M. (2009). Savant skills in
autism: Psychometric approaches and parental reports. Philosophi-
cal Transactions of the Royal Society of London. Series B, Biological
Sciences, 364, 1359-1367. doi:10.1098/rstb.2008.0328
Kaufman, A. S., Long, S. W., & O’Neal, M. R. (1986). Topical review
of the WISC-R for pediatric neuroclinicians. Journal of Child Neu-
rology, 1, 89-98. doi:10.1177/088307388600100202
Koegel, L., Matos-Freden, R., Lang, R., & Koegel, R. (2012). Interven-
tions for children with autism spectrum disorders in inclusive school
settings. Cognitive and B ehavioral practice, 19, 401-412.
doi:10.1016/j.cbpra.2010.11.003
Lachmann, T., Schumacher, B., & van Leeuwen, C. (2009). Controlled
but independent: Effects of mental rotation and developmental dys-
lexia in dual task settings. Perception, 38, 1019-1034.
doi:10.1068/p6313
Lachmann, T., & Van Leeuwen, C. (2007). Paradoxical enhancement of
letter recognition in developmental dyslexia. Developmental Neuro-
psychology, 31, 61-77. doi:10.1207/s15326942dn3101_4
Leeper, R. W. (1948). A motivational theory of emotions to replace “emo-
tions as disorganized responses”. Psychological Review, 55, 5-21.
doi:10.1037/h0061922
Lincoln, A. J., Courchesne, E., Kilman, B. A., Elmasian, R., & Allen,
M. (1988). A study of intellectual abilities in high functioning people
with autism. Journal of Autism and Developmental Disorders, 18,
505-524. doi:10.1007/BF02211870
Mayer, J. D., Salovey, P., & Caruso, D. (2002). Mayer-salovey-caruso
emotional intelligence test (MSCEIT), Version 2.0. Toronto: Multi-
Health Systems.
Mottron, L., Dawson, M., & Soulieres, I. (2009). Enhanced perception
in savant syndrome: Patterns, structure and creativity. Philosophi-
cal Transactions of the Royal Society of London. Series B, Biological
Sciences, 364, 1385-1391. doi:10.1098/rstb.2008.0333
Patterson, K. E., Marshall, J. C., & Coltheart, M. (1985). Surface dys-
lexia: Neuropsychological and cognitive studies of phonological read-
ing. Hillsdale, NJ: Lawrence Erlbaum.
Pivik, J., McComas, J., & Laflamme, M. (2002). Barriers and facilita-
tors to inclusive education. Exceptional Children, 69, 97-107.
Pring, L. (2005). Savant talent. Developmental Medicine & Child Neu-
rology, 47, 500-503. doi:10.1017/S0012162205000976
Reid, C., & Romanoff, B. (1997). Using multiple intelligence theory to
identify gifted children. Educational Leadership, 55, 71-74.
Robertson, K., Chamberlain, B., & Kasari, C. (2003). General educa-
tion teachers’ relationships with included students with autism.
Journal of Autism and Developmental Disorders, 33, 123-130.
doi:10.1023/A:1022979108096
Salovey, P., & Mayer, J. D. (1990). Emotional intelligence. Imagina-
tion, Cognition and Personality, 9, 185-211.
doi:10.2190/DUGG-P24E-52WK-6CDG
Schutte, N. S., Malouff, J. M., Hall, L. E., Haggerty, D. J., Cooper, J. T.,
Golden, C. J. et al. (1998). Development and validation of a measure
of emotional intelligence. Personality and Individual Differences, 25,
167-177. doi:10.1016/S0191-8869(98)00001-4
Siegel, D. J., Minshew, N. J., & Goldstein, G. (1996). Wechsler IQ pro-
files in diagnosis of high-functioning autism. Journal of Autism and
Developmental Disorders, 26, 389-406. doi:10.1007/BF02172825
Spearman, C. (1904). General intelligence, objectively determired and
measured. American Journal of Psychology, 15, 201-293.
doi:10.2307/1412107
Sternberg, R. J. (1985). Implicit theories of intelligence, creativity, and
wisdom. Journal of Personality and Social Psychology, 49, 607-627.
doi:10.1037/0022-3514.49.3.607
Sternberg, R. J. (1997). The concept of intelligence and its role in life-
long learning and success. American Psy c h ologist, 52, 1030-1037.
doi:10.1037/0003-066X.52.10.1030
Szatmari, P., Tuff, L., Finlayson, M. A., & Bartolucci, G. (1990). As-
perger’s syndrome and autism: Neurocognitive aspects. Journal of
the American Academy of Child & Adolescent Psychiatry, 29, 130-
136. doi:10.1097/00004583-199001000-00021
Takahashi, J., & Gyoba, J. (2012). Self-rated autistic-like traits and
capacity of visual working memory. Psychological Reports, 110,
879-890. doi:10.2466/24.02.04.PR0.110.3.879-890
Takahashi, J. (in Press). Suggestions for teacher training program for
inclusive education in Japanese university. Creative Education, 4,
509-513.
Takahashi, J., Kawachi, Y., & Gyoba, J. (2012). Internal criteria un-
derlying affective responses to visual patterns. Gestalt Theory, 34,
67-80.
Torgesen, J. K. (1988). Studies of children with learning disabilities
who perform poorly on memory span tasks. Journal of Learning
Disabilities, 21, 605-612. doi:10.1177/002221948802101004
UNESCO (2001). The open file on inclusive educati on. Paris: UNESCO.
Wechsler, D. (1999). Manual for the wechsler preschool and primary
scale of intelligence-revise d. San Ant onio: Psy cholo gical Corp oration.
Wechsler, D. (1991). Wechsler intelligence scale for children—Third
edition (WISC-III). San Antonio, TX: Psychological Corporation.
Wechsler, D. (1997). WAIS-III administration and scoring manual.
New York: The Psychological Corporation.
Yuste, C., Martínez, R., & Galve, J. L. (1998). BADyG. Technicalma-
nual. Madrid: CEPE.