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The Reading Matrix © 2009
Volume 9, Number 1, April 2009

The Relevance of Multiple Intelligences to CALL Instruction

In-Seok Kim
Dongduk Women’s University


ABSTRACT

Many teachers and researchers believe learning preferences or learning styles can be used
advantageously to enhance language study and motivate learners. Following an overview of
Gardner’s theory of multiple intelligences (MI) and research on multimedia-based approaches
in foreign language instruction, this paper first describes a study comparing students’ learning
preferences, obtained through an MI inventory survey, to their listening scores before and after
CALL instruction. The correlation between students’ MI scores and listening scores is then
analyzed, determining whether their MI was improved by CALL instruction, and if so, which MI
and to what degree. This is followed by a discussion on how language learning software could
be implemented to increase students’ use of multiple learning styles. It is concluded that CALL
software can be effectively used to enhance the many kinds of human intelligences employed
when learning languages.


INTRODUCTION

All learners are endowed with different mind sets that they employ while performing
various activities in their daily lives. These learning styles or learner strategies are believed by
language pedagogues to be essential in deciding the success or failure of language learning
(Reid, 1987; Oxford, 1993; Chamot, 2004; DeCapua & Wintergerst, 2005). This view has been
largely documented in ESL/EFL literature. Working from the discipline of psychological
education, Gardner proposed in the early 1980’s the theory of multiple intelligences (MI) that all
humans are born with different sets of intelligences and tend to excel in one area over another.
When these two views are taken together, the principles of learner strategies can be viewed as
not far removed from the MI theory: Learners have a priori propensities to utilize their inborn or
acquired intelligences in their attempt to discover or learn new things, such as languages. What
matters is how we can facilitate their new learning activities both in and out of the classroom.
Many multimedia experts believe that multimedia technologies can motivate students to
learn languages better than without it (Thornburg, 1997; Cameron, 1998; Brett, 2000; Egbert &
Hanson-Smith, 2007). This is one of the reasons why practitioners across the language curricula
resort to Computer-Assisted Language Learning (hereafter abbreviated as CALL) instruction for

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innovative language teaching, particularly with the advent of modern technologies over the last
decade. Keobke (1998) addresses the issues in designing CALL for multiple language learning
styles. He believes that students can learn languages best when programs are geared to their
learning styles, which is not always possible in traditional classrooms. He advocates
collaborative learning activities—easily provided by the computer—as the best way for learners
to improve their language. Although there are some research reports examining the effectiveness
of learner strategies and multiple intelligences in the multimedia-assisted language classroom
setting (Po-Ying, 2006; Haley, 2004; Christison, 1999a, 1999b), nonetheless, there is a paucity
of research on identifying the mutual relationships among them.
This paper sketches the theoretical underpinnings of the MI theory and the effectiveness
of multimedia technology in the English language classroom. Beginning with a succinct review
of the literature, it subsequently formulates three research questions and describes the procedure
of an experiment that this researcher constructed. It then analyzes the data and discusses the
issues involved in applying MI to CALL instruction. Finally, suggestions are made for future
research to determine how best to utilize students’ multiple intelligences to maximize CALL
instruction in modern language classrooms.


THEORETICAL FOUNDATIONS

On the Theory of Multiple Intelligences

Gardner’s (1983) theory of multiple intelligences (MI) proposes a means to
understanding the many ways in which human beings are intelligent; that is, how we process,
learn, and remember information, in contrast to the prevailing notions of intelligence testing,
which posit a general, all-encompassing general intelligence. Gardner (1983, 2000) states that
while individuals are capable of processing information in at least seven different ways (see
Figure 1), each individual varies in the degree of skill possessed in each of these intelligences.

Figure 1. Seven Types of Human Intelligences (taken from Armstrong, 1999)

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The seven types of MI are as follows:

1. Linguistic intelligence is our ability to speak to each other in our daily conversation,
or write a letter to someone, or perform any verbal activity. This intelligence goes
unnoticed because everyone unconsciously uses it so commonly.

2. Logical-mathematical intelligence is our ability to solve problems and meet new
challenges. This type of intelligence is associated with scientific thinking, such as
measuring the perimeter of the earth or predicting when an area will be hit by an
earthquake. This intelligence is also tied to counting, as when checking change after
making a purchase, or planning something for the future.

3. Musical intelligence is our ability to sing a song or chant to the tune of a radio
melody. We often use this type of intelligence to alleviate stress, but musical
intelligence may also make some students more attuned to accent and pitch in
language study.

4. Intrapersonal intelligence allows us to be independent, appreciate time alone, and
be self-reflective. Intrapersonal intelligence involves knowledge about and awareness
of the internal aspects of self (Lazear, 1999), such as knowledge of feelings, thinking
processes, self-reflection, etc. Study and homework performed in isolation are
intrapersonal.

5. Interpersonal intelligence is expressed in our human relationships where we
cooperate with each other or agree or disagree with each other. The trait of
interpersonal intelligence is the most common intelligence foreign language teachers
use. These include, but are not limited to, caring for others, communicating with
others, empathizing and sympathizing with others, leading and organizing groups,
resolving conflicts, seeing from another’s point of view, and working as a team
member. Kagan (2000) suggests that these skills can be activated by cooperative
learning structures: Think-Pad Brainstorming; Give One, Get One; Round Robin;
Timed-Pair-Share; Fan-N-Pick; Blind Sequencing; and Inside-Outside Circle.

6. Bodily-kinesthetic intelligence requires physical movement such as shoveling snow,
painting pictures, dancing to music, or performing sports. Bodily-kinesthetic
intelligence involves our ability to use the body to express feelings or desires. The
Total Physical Response (TPR) approach to language teaching relies on the use of
bodily-kinesthetic intelligence.

7. Spatial intelligence involves visualization of things or ideas, through which we can
retain memories for a longer period of time. Visual-spatial intelligence enables us to
grasp meanings better when they are traced with visual images. Nelson (2006)
suggests that students synthesize and create new meanings when they combine visual
images and text in, for example, digital storytelling activities.

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Gardner’s (1983) theory of multiple intelligences is not based upon binary attributes—
either linguistic or logical-mathematical, as expressed in the IQ test formula prescribed by Binet
(1916) and Binet and Simon (1916) and his disciples. However, Gardner believes that
individuals may rely more heavily on one intelligence over another. Accordingly, foreign
language teachers may help students learn better by tapping into one or more of the intelligences
that an individual student might use dominantly. In the years since Gardner first published
Frames of Mind (1983), research has connected multiple intelligences and learning styles (Kean,
2005), and multiple intelligences and learning strategies (Felder & Soloman, 1999). In particular,
a number of articles have explored the possibility of applying multiple intelligences to the
teaching of English to grade school students (Son, 1998; Kim, 2000; Boo & Choi, 2000). This
research has shown that applications of MI theory have had a positive influence on learning
English in class and enhanced students’ interest in language learning. Likewise, a number of
teachers and researchers (Kolb, 1984; McCarthy & McCarthy, 2005) treat MI as the basis for
learning preferences or learning styles that can be used advantageously by teachers to enhance
language instruction and motivate learners. (Subsequent to his initial work on MI, Gardner
(1999) has spoken of moral and spiritual intelligences and other types of intelligences, which this
paper disregards as being less easily definable as aids to language learning.)

Multimedia Technology and CALL Instruction

Digital technology has enabled multimedia to be an important part of our lives and has
become an integral part of the educational profession in the 21st century. We have seen
multimedia applications (for example, on CD-ROMs, Web pages, laser videodisks, etc.) make a
great impact on foreign language learning. Computer-based multimedia is a very appealing tool
for education since it can deliver combinations of text, sound, graphics, still images, animations,
and video. Multimedia can, if properly used, make a great impact on foreign language learning
since each media element has its own particular advantage in conveying different kinds of
messages and evoking various kinds of learner responses (Brett, 1996).
There is a great deal of research examining whether multimedia is effective for language
learning. Brett (1998) summarizes the most important perspectives from which to view the
effectiveness of multimodality: intuitive, second language acquisition theory, and empirical
research. From the intuitive perspective, Brett argues that the power and effectiveness of adroitly
aligned multiple learning media should be greater than the sum of their individual parts. The
second perspective, second language acquisition theories (such as input theory, noticing,
motivation, and autonomy) are well documented in the body of SLA literature. His third
perspective is rooted in the findings of empirical research that examine the effectiveness of
multimedia language learning programs (Liu & Reed, 1994; Chun & Plass 1996; Grezel &
Sciarone, 1994; Brett, 1996, 1997, 1998). His review of the literature strongly supports the claim
that multimedia-assisted language instruction has proven to be more effective than non-media-
supported methods of language teaching.
As concerns MI theory and practice, an increasing number of educators have discussed
the practical application of MI in foreign and second language classrooms (Kolb, 1984;
McCarthy & McCarthy, 2005; Nelson, 2006). In particular, Christison (1998a, 1999a, 2005) has
worked extensively on integrating MI theory into second/foreign languages classroom teaching.
Her body of work includes a taxonomy of language-learning activities of multiple intelligences, a

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TESL/TEFL MI weekly syllabus, the creation of MI-based lesson plans, examples of four stages
of information processing, and an MI assessment. Other research (Christison, 1999b) describes
how MI theory can be applied in teaching English as a second language to adults. She
recommends first identifying adult ESL students’ strengths and learning preferences and then
allowing them to employ appropriate MI strategies in language learning. She states that students
are likely to become more engaged in learning as they use learning modes that match their
intelligence strengths. Berman (1998, 2001) exemplifies the variety of hands-on exercises and
activities available for each type of intelligence in the MI theory. Another study comes from
Halley (2004) whose work with a large number of K-12 students indicates that students achieve
greater success when MI theory was implemented in classroom teaching. Currie (2003)
addresses a dire need for ESL teachers to identify their students’ strengths and weaknesses in
order to make a greater impact on their language learning. She argues that teachers should
encourage students to use their strengths, which can be identified by giving a simple MI
questionnaire, in order to make the learning process more accessible.
A similar story comes from numerous articles on MI-based teaching with computers.
Dryden (2004) offers a thorough explanation of MI theory and interprets CALL in light of the
theory. He holds the view that CALL offers something for nearly everyone because multimedia,
as seen in hypertext on CD-ROMs and the World Wide Web, integrates several intelligences and
allows them to support each other. He further believes that the enfranchising power of CALL and
MI theory will transform the ways we teach in schools by bringing a new curriculum adaptable
to each student’s needs in the 21st century. Another recent research paper in a similar vein is
Stedge’s (2005) article describing how she designed her French long distance learning course in
tandem with the theoretical underpinnings of MI theory. This research merits our attention in that
she used videoconferencing to teach French online. She believes that videoconferencing or
steaming video can naturally help to further the use of MI theory for the benefits of
nontraditional learners, especially for poor readers and writers. Her belief relies on Trotter’s
(1993) strategies that emphasize collaboration, independent thought, creation, and exploration.
These four ways of learning with others comprise MI theoreticians’ stock in trade, and they, in
turn, represent the essence of what learners can do with multimedia.
As concerns student attitudes, Chen (2003) reports students’ positive reactions to CALL
instruction. He asked students to practice English, using the Tell Me More CD-ROM for one
hour per week in a weekly three-hour course. Students’ questionnaires at the end of the second
semester revealed that the use of courseware enhanced their pronunciation, conversation, and
listening abilities. Chen’s (2004) ensuing research reports the use of MI theory in large
computer-assisted EFL college classes in Taiwan. In this research, he taught students for one
hour with technological activities in English in a multimedia classroom and with reading,
writing, and discussion for two hours in a regular classroom. At the end of the course, he asked
students to present their reading activities from the textbook through multimedia, using six types
of intelligences. He reports students’ high motivation as well as intense affective responses. This
experience assured him that the collaborative pedagogy essential to MI theory and multimedia
had proven to be effective in promoting individualized and student-centered learning.
Turning to curriculum design, Mackenzie (2002) demonstrates how educators can
harness the power of technology in their instructional planning. He believes that the integration
of multimedia technology into the classroom needs no longer to be a hit-or-miss proposition. He
compares MI with non-digital technologies to MI with digital technologies to demonstrate the

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unprecedented change in the landscape of technology in the educational environment over the
last 30 years. Teaching and learning through multiple intelligences (Campbell, L., Campbell, B.,
& Dickinson, 2004) is a seminal work in integrating MI into teaching languages. This book
offers practical classroom applications of the MI theory, and includes ample resources for
incorporating MI into the teaching of languages through multimedia technology. More extensive
resources for teachers can be found at the International Society of Technology in Education
(ISTE) Website (www.iste.org) and Riverdeep’s The Learning Company (www.riverdeep-
learning.co.uk). The ways in which MI may be enhanced by computer-assisted instruction, as
proposed by these various works, are summarized in Table 1.

Table 1. MI Enhanced by CALL Activities

Intelligence
CALL Activities

keyboarding, practicing language skills with interactive software or on
Websites, using word processors, using spelling and grammar checkers,
Linguistic
creating multimedia reports, writing and reading email, text and
videoconferencing, using speech recognition devices, using a concordancer,
using translation software or Websites, using the Web for research


Logical-Mathematical
using software or Websites with brain teasers, puzzles, games of logic, etc.


playing card games, using graphics programs, learning with pictures on CD
Spatial
or DVD or with video clips on the Web, using presentation software,
creating videos or digital storytelling products


playing computer games, using TPR-based instructional software, using
Bodily-Kinesthetic
simulation software or virtual reality environments on the Web


listening to and interacting with songs on software or on the Web,
Musical
composing digital music live or on interactive Websites


using email, text or voice chatting, using cell phones and PDAs, engaging in
Interpersonal
computer-supported collaborative learning (e.g., e-pals or the GLOBE
project)


using intelligent tutoring systems, using speech recognition devices, using
Intrapersonal
news groups, meta-cognitive journaling or blogging, using mind-mapping
software or Websites, learning about computers using software or Websites



In sum, all previous research points to symbiotic interdependency between MI theory and
multimedia-assisted CALL instruction. Probing this relationship was the focus of the
investigation presented next.

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RESEARCH METHOD

Research Questions

In the previous section, we have seen the relationship between MI and multimedia
technologies used for language instruction. Based on this literature review of MI theory and
multimedia CALL instruction, three research questions concerning this relationship were
proposed:

(1) To what degree does CALL instruction increase students’ listening scores?

(2) Does CALL instruction improve students’ MI inventory scores and, if so, which type
of MI quotients will increase the most?

(3) Which particular type of intelligence correlates most highly with listening ability?

Methodology

Subjects

Thirty-nine juniors and seniors majoring in English Language and Literature at Dongduk
Women’s University participated in the experiment. These students, averaging 22 years of age,
were enrolled in the 16-week multimedia technology-assisted language course that this
researcher taught in the Spring semester of 2006.

Instruction and Testing

Pre-test

During the first meeting of the class, students took the Test of English for International
Communication (hereafter abbreviated as TOEIC) listening test (Part I and Part II, 50 items) to
determine their general listening abilities. Students were also given a Korean MI questionnaire
that the researcher modified from existing MI inventories (Christison, 1998a; Armstrong, 1999).
This questionnaire consisted of ten sections, eight items per section. (In addition to querying the
seven intelligences described earlier in the paper, the researcher included an eighth, Natural
intelligence, proposed in Gardner’s (1999) later work; these results were negligible and are not
included in this study.) All items were chosen at random to disguise their nature. Students were
asked to mark 5 (strongly agree), 4 (agree), 3 (undecided), 2 (disagree), or 1 (strongly disagree)
for each item in the questionnaire. The MI questionnaire translated into English from the original
Korean version appears in Appendix A.

Provision of CALL Instruction

In accordance with the weekly syllabus found in Appendix B, the researcher taught from
March 2 to June 16 of 2006 a general English course covering elements of all essential language
skills (i.e., reading, writing, listening, speaking, grammar, and vocabulary). The course syllabus
consisted of sixteen weeks: fourteen weeks of teaching and two weeks of exams.

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Allocation of Instructional Time

Though the course content was distributed over different class time allotments, no class
time was allotted to teaching the Bodily-kinesthetic or Musical intelligences as seen in Table 2.
The researcher had to allocate different amounts of time to teaching with a focus on different
types of intelligence due not only to the course objectives but also to the resources available. In
determining the class time allotment, the researcher analyzed the contents of materials from his
CALL instruction for each class period and calculated the amount of time spent on CALL
instruction linked to each type of intelligence. For example, the class time spent on each
intelligence ranges from 40% for Linguistic, 20% for Spatial and Intrapersonal, and 10% for
Logical-mathematical and Interpersonal intelligences respectively. The class time spent on the
Bodily-kinesthetic and Musical intelligences was zero since they were not included in the course
syllabus.

Table 2. Class Time Allocation

Class Time
Type of Intelligence
CALL-Based Activities
(%)

lectures, small discussions, worksheets, memorizing,
Linguistic
using word-processors, listening to sounds and
40
recording their pronunciation, quizzes


videos, animations, pictures, visual awareness
Spatial
20
techniques


computer-assisted training, word-processing classroom
Intrapersonal
20
assignments, individual project, goal setting


logical and sequencing presentations, logical guessing,
Logical-Mathematical
10
word problems, puzzles, games


Interpersonal
group discussions, pair work, group brain-storming
10


Bodily-Kinesthetic
None
0


Musical
None
0



Post-test

At the end of the course, students were given the same 50 items TOEIC listening test to
determine any increase or decrease in students’ general listening abilities when compared with
their test before the course started. Students were also given the same MI questionnaire to
compare with the result of the first MI questionnaire they took at the beginning of the course.

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RESULTS

The two sets of TOEIC listening scores and MI inventories were entered into the SPSS
for Windows (SPSS, 2005) program to address the two research questions under investigation.
The first question was whether the researcher’s CALL instruction would increase students’
listening scores. The mean and standard deviation (SD) of students’ listening scores are shown in
Table 3.

Table 3. Mean and Standard Deviation of Listening Scores


N = 39
Mean
SD
P level


Before CALL Instruction
28.38 (50)
5.348 (50)


<0.000
After CALL Instruction
32.61 (50)
5.178 (50)


The students’ TOEIC listening mean score is 28.38 on a scale of 50 maximum before
CALL instruction and 32.61 thereafter. The difference between these two scores is deemed
significant at the p<0.05 level. Thus, one can say that students’ listening scores improved during
CALL instruction. However, it may not be possible to argue that CALL instruction was more
effective than non-CALL instruction since this course was not compared with a similar non-
CALL instruction course.
The second question investigated whether CALL instruction would improve students’ MI
quotients, and if so, which type of MI quotients would increase the most. Table 4 shows the
mean scores and SDs of all types of MI before and after CALL instruction.

Table 4. MI Quotients before and after CALL Instruction


Paired T-test Results
N = 39
Before
After


Mean
SD
T-value
P level


Mean
2.02
2.24
.224
.199
7.04
.000


SD
.37
.40
-
-
-
-


The average of the seven MI quotients combined is 2.02 before CALL instruction, but
increased to 2.24 thereafter. This difference (.22 increase) is statistically significant at the p<.05
level. Thus, it reasons that CALL instruction has improved students’ MI quotients in general.
Considering which type of MI has improved most or least, Table 5 shows the mean score
and SD of each type before and after CALL instruction. The maximum score for each type
before or after CALL instruction was 5 points.

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Table 5. Mean and Standard Deviation of Each MI Type after CALL Instruction


Before
After
Paired T-test Results
MI Type
(maximum (maximum





5 points)
5 points)
Mean
SD
T-value
P level











Linguistic
1.97
2.25
.279
.301
5.798
.000


Logical-Mathematical
1.68
1.94
.261
.331
4.929
.000


Spatial
2.00
2.29
.284
.389
4.561
.000


Musical
2.18
2.33
.158
.342
2.904
.006


Bodily-Kinesthetic
1.85
2.00
.138
.395
2.185
.035


Interpersonal
2.22
2.47
.226
.333
4.277
.000


Intrapersonal
2.58
2.79
.212
.338
3.922
.000


Table 5 shows that the mean scores of all seven MI types have improved following
CALL instruction, albeit to varying degrees. For example, the mean score of Linguistic is 1.97
before CALL instruction and increased to 2.25 thereafter. The difference between the two is .279
and its SD is .301. This difference is statistically significant at the level of p<0.05. It should be
noted, however, that the degree of improvement following CALL instruction varies. Figure 2
shows the mean score increases of the seven MI types following CALL instruction in descending
order. The points on the left indicate the increase of MI Quotients following CALL instruction.
As seen in Figure 2, the score improvements can be divided into three groups. The first
group consists of Spatial, Linguistic, and Logical-mathematical. The relative high score
improvement of the three MI types in this group appears to be closely related to CALL
instruction, because these first three MI types were essential elements of this researcher’s
syllabus and teaching approach. In fact, CALL instruction in this class heavily relied on visually-
driven language materials (e.g., pictures, sounds, animated figures) for Spatial stimulation,
intensive Linguistic practice (e.g., repetition, role-play, pronunciation practice, pair practice) in
the course of language study, and thinking skills development (e.g., paragraph sequencing,
guessing games, word problems) for the Logical-mathematical side. These were all performed on
computers.
The second highest group consists of Interpersonal (22.6), and Intrapersonal (21.2). The
score improvements in these two intelligences were expected since the technology-based
instruction entailed some degree of these two elements. For the former, students were given
frequent opportunities to practice language in pairs, in groups, and through role-play. These
activities were completed without computer-assistance. For Intrapersonal study, students
practiced language using individually assigned multimedia programs running on the computer
for ten to fifteen minutes of each class period. This practice included talking to the characters on
the screen using paired branching dialogue techniques, answering comprehension questions after
watching a video clip, grammar and word study with pictures, and pronunciation practice using

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