The document reviews research on the influence of multiple intelligences (MI) on academic achievement, suggesting moderate support for a link between MI and improved learning outcomes. It discusses various studies, including brain-based learning and cooperative methods aligned with MI theory, but highlights a need for valid instruments to measure MI effectively. Finally, it argues that future research should focus on high-quality learning processes rather than solely on quantifiable academic results.

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Running head: EFFECTS OF MULTIPLE INTELLIGENCES ON ACADEMIC ACHIEVEMENT
Effects of Multiple intelligences
on Academic Achievement
Quinn Collor
Chapman University
In partial fulfillment of the requirements for EDUU 606
Dr. Sinon O’Halloran
May 27, 2007

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Abstract
How does multiple intelligences influence academic achievement? Using the University of
Chapman Library, a review of the research reveals moderate support suggesting a link between
multiple intelligences (MI) and academic achievement. While the research fails to prove causality
for achievement, the evidence supports MI to explain a culture of diversified learners. Moreover,
traditional methods of teaching and multiple intelligence theory share some common learning goals
such as improving motivation, effort, cognitive power, performance goals, and a positive learning
climate. Further research would help clarify a definitive use of MI as a tool for measuring process
rather than product. In addition, a valid and reliable instrument to test the effectiveness of multiple
intelligence theory is suggested.

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Effects of Multiple intelligences
on Academic Achievement
In the article, The Effect of Brain-Based Instruction to Improve on Students’ Academic
Achievement in Social Studies Instruction (Duman B., 2006), researchers aimed to determine the
effects of brain-based learning on increasing student’s academic achievement in social studies. This
study comprised three major groups of sixth grade children receiving social studies instruction.
Each group consisted of about 40 students. Researchers gave traditional instruction methods to one
group while the other two received Brain-Based Learning (BBL) instruction. One of the guiding
principles of BBL stresses individual uniqueness in terms of learning. This is also one of aims of
Multiple Intelligence theory. Successful BBL depends on the environment and procedures that
allow the student to maximize their learning experience. For example, using the principle of
movement, students could move around at will sitting with whomever they want or even wander
about the classroom. Several guidelines unique to BBL make up most of the instruction. Underlying
principles for this study included, A) Students receive a computer-assisted audio-visual presentation
about how the brain works and learns. B) Adopt the approach of producing information rather than
memorizing it. C) Adopt the methods of scientific research and problem solving. D) Since feelings
play a critical role in BBL and instruction, the lesson atmosphere is an important element for
success.
According to Duman, BBL involves accepting the rules of brain processing and organizing the
teaching according to these rules in the mind for meaningful learning. Teaching strategies that
enhance brain-based learning include manipulative, active learning, field trips, guest speakers, and
real-life projects that allow students to use many learning styles and multiple intelligences (Duman,

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2006). The results revealed that there were meaningful differences amongst the three groups.
However, there were no significant differences between genders. Duman articulates that BBL or
brain-compatible learning focuses on concepts that create an opportunity to maximize the transfer
of knowledge, attainment and retention of information. Furthermore, enriched and challenging
environments produce more neural connections while boring and sterile connections whither and
die (Duman, 2006). In addition, the students specifically stated they were able to analyze
themselves better thanks to having received information about their brains.
In the article, Multiple Intelligences and Reading Achievement: an Examination of the Teele
Inventory of Multiple Intelligences, the intent was to test the reliability an instrument designed to
determine the presence of a particular multiple intelligence and determine its effectiveness on
reading achievement. The instrument tested was the Teele Inventory for Multiple Intelligences
(TIMI) developed by researcher Sue Teele. Teele recognized the need for determining which
multiple intelligences are more pronounced than others are. This would help guide a teacher in
selecting instructional strategies and appropriate methods for a particular intelligence (e.g. spatial,
linguistic, musical etc.). The TIMI consists of 28 pairs of black and white drawings of pandas
engaged in various activities (e.g. reading and roller skating). Each activity is related to one of
Gardner’s Multiple Intelligences.
The study involved 288 fourth grade students at two Illinois school districts. The reading
comprehension test was the nationally standardized Gates-MacGinitie Test of Reading. The results
showed that the TIMI did not correlate to how well a student did on the reading test. Interestingly,
the only intelligence that was related to reading was the logical-mathematical intelligence. The
relationship was not particularly strong. Moreover, without a reliable valid assessment of Multiple
Intelligences (MI), interventions that purport to change or improve MI cannot be appraised directly.

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Whereas the TIMI was a promising tool from the viewpoint of ease of use, its measurement
characteristics do not support continued use by educators (McMahon & Rose, 2004).
In the article, The Relationship between Learning Styles/ Multiple Intelligences and Academic
Achievement of High School Students (Snyder, R. F. 1999), an experimental instrument based on
cognitive psychology research was constructed to examine the effects of using this instrument on
increasing academic achievement. The instrument is comprised of student profile information
including questions about a preferred mode of processing; Auditory, Visual, and Tactile/Kinesthetic
Learners; and Analytical and Global Learners. In addition, self-perception preferences, such as
motivated by self or others, works by self or with others, prefers formal or informal settings, or
prefers quiet or noisy settings were studied. A student could choose self-describing characteristics
such as persistence or responsibility or whether they preferred structure in material presented to
them. The instrument measures the following Multiple Intelligences: Linguistic, Logical, Spatial,
Bodily/Kinesthetic, Musical, Interpersonal, and Intrapersonal. The concurrent validity of the
instrument was checked by comparing the learning styles results of the instrument with the
Learning Styles Profile developed by the National Association of Secondary School Principals
(NASSP) and comparing the Multiple Intelligences results of the instrument with the Multiple
Intelligences Inventory developed by researcher and author Dr. Thomas Armstrong (Snyder, 1999).
Student information from recent grading and assessment records provided an analysis of the
data. The following academic achievement data was collected: the students' Grade Point Average
(GPA); the students scores on the most recent Metropolitan Achievement Test (MAT-7)-Reading,
Mathematics, and Total; and the students' scores on the state's Basic Skills Assessment Profile
(BSAP)-Math and Reading (Snyder, 1999). Since this study was involved only with existing
conditions and no direct intervention was involved, the primary statistical evaluations involved the

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correlation between learning styles/ multiple intelligences and academic achievement (Snyder,
1999).
The results were observed by comparing correlations between Grade Point Average (GPA) and
each category or instrument. For example, there is a positive correlation between the students'
Grade Point Averages and the categories of persistence, visual, self motivated, responsible, and
prefer to work with sound, and prefer to work alone. For the male students, there is a positive
relationship between the students' Grade Point Averages and the categories of spatial, prefers
working alone, self-motivated, visual, and logical. There is a negative correlation between the
students' Grade Points Averages and the categories of preferring sound and preferring to work with
others (Snyder, 1999).
In the article, The Effects of Cooperative Learning within a Multiple Intelligence
Framework on Academic Achievement and Retention in Math, (Yildirim, K., Tarim, K., Iflazoglu,
A., 2006), researchers aimed to evaluate the effect of a cooperative teaching method based on
Multiple Intelligence Theory on the academic achievement and retention of mathematic lessons of
primary school students. Testing lasted seven weeks with 46 fourth grade students of a low socio–
economic level at a state elementary school in Turkey (Yildirim et. al., 2006).
The control group had traditional means of instruction while a second group used the
cooperative method. All students took the Teele Inventory Multiple Intelligence (TIMI). The TIMI
refers to a diagnostic inventory revealing a best-fit strategy for a student’s preferred learning style.
Teachers gave the experimental group an awareness-raising program to help students focus on
being cooperative. The results showed that the cooperative learning method supported by multiple
intelligence theory is more effective than the whole class teaching on achievement. At the same
time, it was found that there are no significant differences in their retention (Yildirim et. al., 2006).

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Included in this literature review is an Action Research study entitled Improving Student
Academic Reading Achievement through the Use of Multiple Intelligence Teaching Strategies
(Reidel, J., Tomaszewski, T., Weaver, D., 2003) from Saint Xavier University in Chicago, Illinois.
This three-month study was comprised of 10-11 year-olds in the fifth grade. The objectives were to
increase reading achievement through curriculum planning and implementation utilizing multiple
intelligence theory. The students were given diagnostic reading tests before and after the study. The
curriculum included content intertwined with student choice, reading centers that emphasized
multiple intelligence learning, student attitude surveys, multiple intelligence activities, portfolio
generation, and organizational procedures designed to maximize student exposure to multiple
intelligences. The products and processes within the intervention, along with a rubric for
assessment, provided the overall structure of the study. The Reading skill assessment test developed
by the school district is standards based.
The reading comprehension pretest and posttest scores showed significant increase in the five
different concentration areas of reading skills (Inference, draw/conclusion, compare/contrast,
sequence, cause/effect). The results also produced positive results due to increased levels of
motivation on the part of the student. The increased motivation was seen as a by-product of student
choices in reading selection and specific skill reinforcement. This approach allowed students to
learn while differentiating about one’s individual strength and weaknesses through creative choices.
Discussion
Multiple Intelligences are useful tools for reconfiguring how and what we teach. Since the
dawn of “chalk” and “talk” teaching, learning has tapered to a trickle as far as brain potential. With
the swift ushering of Brain-based learning (BBL) into the educational arena, educators may soon
have an easier job shifting responsibilities from teacher to facilitator. Duman (2006) clearly points

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to accepting the rules of the brain to make learning more meaningful. Moreover, BBL adopts the
approach of producing information rather than memorizing it. These assumptions are clearly
important and relevant to 21st century teaching. Curriculums focused on using the latest BBL
instructional methods and strategies might easily outpace the last fifty years of conventional
learning in record time. It is as if someone just handed you the manual, “Here’s how your brain
really works and how to drive it”. The ramifications of BBL for education are promising to say the
least. Technology will play a major role (as it usually does) in tailoring educational needs with
more speed and precision.
Multiple Intelligences (MI) represents a pivotal shift from one-dimensional learning to a multidimensional model. If we accept multiple intelligence theory to replace the old paradigm, the theory
must do more than predict the preferences of human intelligence. Achievement results must be
clearly linked to theory and the instrument must be tested in many contexts to prove causality.
Research by Macmahon (2004) reveals a flaw in the instrument used to identify the various
intelligences in MI theory. The Teele Inventory for Multiple Intelligences (TIMI) failed to show
how well a student did on a reading test. Since most of the articles reviewed made reference to the
use of TIMI, creating a valid and reliable instrument to measure MI is of paramount importance.
Most studies reviewed relied upon multiple instruments, strategies, and methods to measure
academic achievement rather than test MI independently. Most researchers seemed intent on finding
a positive reason to use MI, but few if any did a thorough job proving a direct relationship to
academic achievement.
I believe Multiple intelligence theory needs to measure the process of using MI. Learning is not
intrinsic to the defined categories of MI. Learning happens by creating a rich high quality process
oriented experience for the student. Snyder (1999), used MI to build student profile preferences, but

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then used Grade Point Averages (GPA) to determine academic achievement. The basic mindset of
the researcher is to prove quantitative causality. The problem for much of the research reviewed is
that much of it compartmentalizes MI forcing it to prove causality rather than treat it like an
instrument to create high quality processes.
Measurements in Standards Based Education (SBE) focus on the product test scores and
averaging as a measure of achievement. If there is a heavy emphasis on creating a product, and not
the processes involved, the product will be compromised. I believe the key to a successfully using
MI, rests with the ability to qualitatively, and quantitatively measure the process of learning. The
effectiveness of a process may be measured by product output. However, as teachers are confronted
each year with a new set of students, new products are created. If a teacher uses the same process
year in year out, it may not work. What becomes important is that students go through a process
that yields a rich learning experience, not just the creation of output. The product becomes less
important because the process is pivotal to understanding what is to be learned. Process or
experience is where the learning occurs. High quality processing is a place in space and time that is
rich with meaning and content. Nothing else matters for the moment except the quality of that
experience. Can high quality processes be measured in MI reliably? If there is a way to measure it,
can high quality processes cause an increase in academic achievement? These questions address
how we might use MI for successfully educating students. Discovering the high quality processes in
MI is what research should be measuring. We have to remember that MI might be of better service
to us if it is seen as a qualitative tool rather than a quantitative one.

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References
Duman, B. (2006). The effect of brain-based instruction to improve on students’ academic
achievement in social studies instruction. Retrieved May 21, 2007, from
http://fie.engrng.pitt.edu/icee2006/papers/3380.pdf
MacMahon, S. D.,& Rose, D.S. (2004). Multiple intelligences and reading achievement: an
examination of the Teele inventory of multiple intelligences.
Journal of Experimental Education, 73, 1(Fall 2004), 41-53.
Reidel, J.,& Tomaszewski, T.,& Weaver, D. (2003) Improving student academic reading
achievement through the use of multiple intelligence teaching strategies.
Retrieved May 14, 2007, from
http://eric.ed.gov:80/ericwebportal/custom/portlets/recorddetails/detailmini.jsp?_nfpb=tr%09
ue&_&ericextsearch_searchvalue_0=ed479914&ericextsearch_searchtype_0=eric_%09
accno&accno=ed479914
Snyder, R. F. (1999). The relationship between learning styles/multiple intelligences and academic
achievement of high school students. High School Journal, 83,2(Dec 1999), 11-20.
Yildirim, K.,& Tarim, K.,& Iflazoglu, A. (2006). The effects of cooperative learning within a
multiple intelligence framework on academic achievement and retention in math.
Journal of Theory and Practice in Education, 2, 81-96.