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Educational Administration: Theory and Practice
2024,30(2), 598-611
ISSN:2148-2403
https://kuey.net/ Research Article
Integrating Neuro-Linguistic Programming And Multiple
Intelligences In Language Learning: A Bridge Between
Theory And Practice
Asiqur Rahaman1*, Dr. Pragyan Paramita Pattnaik2
1*HSS, Research Scholar, C. V Raman Global University, India, *ashikrahaman786@gmail.com
2HSS, Professor, C V Raman Global University, India
Citation: Asiqur Rahaman (2024), Integrating Neuro-Linguistic Programming And Multiple Intelligences In Language Learning: A
Bridge Between Theory And Practice, Educational Administration: Theory and Practice, 30(2), 598-611
Doi: 10.53555/kuey.v30i2.1627
ARTICLE INFO
ABSTRACT
Received- 12/03/2024
Accepted- 02/04/2024
This research delves into the benefits of combining Neuro Linguistic Programming
(NLP) and Multiple Intelligences (MI) in language learning. NLP utilizes language,
behavior and strategic thinking to improve communication and learning while MI
theory identifies eight types of intelligence fostered by tailored learning
environments. The study extensively reviews existing literature, on NLP and MI
focusing on the possibilities and obstacles of integrating them into language
education. It demonstrates how the fusion of NLP and MI can establish learning
settings that cater to learning preferences. This research contributes to the field of
language learning by connecting theory with applications. Through the use of SPSS
software for data analysis it examines hypotheses, investigates relationships between
variables. The results indicate that incorporating NLP and MI in language learning
shows potential for addressing differences and preferences ultimately improving
outcomes, for learners and educators in a language learning setting.
Keywords: Neuro-Linguistic Programming (NLP), Multiple Intelligences (MI),
Language Learning, Theory and Practice, Personalized Learning, Educational
Strategies
Themes: Inclusive Education, Educational Psychology and Learning Styles
1. Introduction
The combination of Neuro Linguistic Programming (NLP) and Multiple Intelligences (MI), in the field of
English language learning has gained attention and importance. This study aims to investigate the advantages
of integrating these two approaches and bridging the gap between theory and practice in English as a
language education.
1.1 Neuro-Linguistic Programming (NLP): A Brief Sketch
Neuro linguistic programming (NLP) is an approach that involves studying the tactics utilized by individuals
and applying them to achieve personal goals. Richard Bandler and John Grinder developed NLP in the 1970s.
NLP delves into the connections, between our thoughts (neuro) our communication (linguistic) and patterns
of behaviour and emotions (programming).
In relation to learning English NLP techniques have been employed to enhance communication skills
accelerate language acquisition and boost self-assurance. NLP can be used to comprehend and modify
thought patterns, language usage and behaviours that may impact success in language learning.
The growth of NLP in English language learning can be attributed to its focus on communication and its
potential to address barriers that may hinder acquiring a new language. Some educators and learners have
found NLP techniques advantageous when it comes to building rapport overcoming language anxiety and
developing motivated attitudes towards learning a new language.
It is worth noting that although NLP has gained popularity in circles as well as self-help domains there is on-
going debate, among psychologists and researchers regarding its scientific validity and effectiveness. Like any
Asiqur Rahaman / Kuey, 30(2), 1627 599
approach peoples experiences with NLP may vary so it's important to approach its use in language learning
with a mind and a critical perspective.
1.2 Multiple Intelligence: An Overview
Multiple Intelligence is a theory that suggests human intelligence is not a single unified entity but rather a
combination of various intelligences. These intelligences are somewhat independent, from each other and it
can be developed through experience and education.
In the context of learning English, Multiple Intelligence theory proposes that there are ways individuals can
learn and process language. Some individuals may excel in learning while others may prefer kinesthetic
methods. By recognizing the intelligences involved in language learning educators can tailor their teaching
approaches to suit each student’s needs.
There are models of Multiple Intelligence. One of the most well-known theories is Howard Gardner’s eight
intelligences. These intelligences include;
1. Linguistic intelligence: the ability to comprehend and use language effectively.
2. Logical mathematical intelligence: the ability to reason, analyse and solve problems.
3. Spatial intelligence refers to the capability of perceiving and manipulating relationships effectively.
4. Bodily kinaesthetic intelligence involves controlling body movements and utilizing them in a skilful
manner.
5. Musical intelligence encompasses the ability to create and appreciate music.
6. Interpersonal intelligence relates to understanding and effectively interacting with others.
7. Intrapersonal intelligence involves comprehending oneself and one’s own emotions.
8. Naturalist intelligence pertains to understanding and engaging with the world.
The theory of Multiple Intelligences has had an impact, on the field of education. It has led to the
development of teaching methods that cater to learning styles. By recognizing these intelligences involved in
language learning educators can assist students in reaching their potential.
1.3 An integration of Neuro-Linguistic Programming (NLP) & Multiple Intelligence
By integrating NLP and MI, the Neuro Linguistic Programming (NLP) VAKOG model and the Multiple
Intelligences (MI) theory are two frameworks that can help us understand and cater to the learning
preferences and strengths of individuals when it comes to English as an second language. VAKOG stands for
Visual, Auditory, Kinesthetic, Olfactory and Gustatory representing the ways people perceive and
communicate with the world (Bandler & Grinder 1979; O’Connor & Seymour 1990). On the hand MI proposes
eight types of intelligence that individuals possess in varying degrees, including logical mathematical, spatial,
musical, bodily kinaesthetic, interpersonal, intrapersonal and naturalist intelligences (Gardner 1983; 1999).
By combining both VAKOG and MI approaches, in teaching English language learners or teachers can
identify a learner’s modality and intelligence type. This understanding allows them to employ strategies and
methods that facilitate learning. For example if a learner is predominantly spatial oriented they may find it
beneficial to use aids such as images graphs maps or videos to grasp new concepts and skills. Alternatively a
musical learner may prefer listening to podcasts songs or dialogues when acquiring vocabulary or grammar.
Individuals, with a preference for bodily kinesthetic learning may find joy in utilizing gestures, movements
and role plays to grasp expressions and situations. Those who lean towards olfactory and naturalist learning
might associate smells. Tastes with words and phrases to gain insights into cultures and cuisines. Similarly
individuals inclined towards gustatory and interpersonal learning may choose to share food and drinks with
others as a means to acquire languages and social skills (Smith, 2008).
The integration of VAKOG (Visual, Auditory, Kinesthetic, Olfactory, and Gustatory) modalities along with MI
(Multiple Intelligences) can also assist learners in developing their areas while utilizing their ones. For
instance if someone lacks logical mathematical skills they can enhance them by listening to explanations or
engaging in puzzles and quizzes. They can also employ numerical mnemonics as aids for retaining
information. Similarly someone lacking proficiency in linguistic skills can improve through reading books,
magazines, comics while employing verbal imagery techniques for better recall (Smith, 2008).
The integration of VAKOG modalities along with MI serves as a tool for educators and learners alike, in
enhancing the quality and effectiveness of the learning process. However it is crucial not to view this
integration as a deterministic framework; instead it should be seen as a guide that can be adapted according
to various contexts, objectives and individual preferences.
It is important to exercise caution and engage in thinking when using it. It should not be seen as a
replacement, for founded principles and practices (Smith, 2008).
2. Statement of problem:
English language learning and teaching is always a challenge for teachers and students. With change of time
pedagogy has been updated by various experts in the same field. Teaching and imparting the understanding
of English language has become a tedious task for language teachers of Cuttack and Bhubaneswar in Odisha,
India. The teachers and students do not know the theory and application of NLP and MI in the field of
600 Asiqur Rahaman / Kuey, 30(2), 1627
education and especially in English language learning for Gen. Z students. In recent days, with barriers and
restrictions, ESL learners and teachers face many challenges such as:
• There is a decline in interest and motivation among students of Generation Z in learning English,
especially in Odisha, Bhubaneswar & Cuttack.
• There is a misconception among students that they are already proficient in English and that language
learning is not important for their career development.
• There are various linguistic and cultural factors that influence the students’ English language acquisition,
such as multilingualism, multiculturalism, and native culture and language.
3. Literature Review:
Dolati and Tahriri (2017) This study examine how the intelligence types dominates of English as a Foreign
Language (EFL) teachers relate to the activities they use in their classrooms. The researchers also investigate
the teachers’ perspectives on multiple intelligences theory. To gather data they employ a mixed methods
approach, including observation interviews and an intelligence survey. The sample consists of 30 EFL
teachers who all teach from the textbook using the same teaching method. The findings indicate that only
teachers with intelligence tend to utilize activities that align with their dominant intelligence type. Other
intelligence types do not seem to influence classroom practices for these teachers. Furthermore the study
suggests that while most teachers possess a view of multiple intelligences theory they lack knowledge and
training in applying it effectively in their instruction. This research contributes insights into the relationship
between multiple intelligences theory and its implications for EFL teaching. Additionally it offers suggestions,
for research and teacher education in this area.
Farahani, F. (2024) In this study the focus is on how NLP techniques impact the reading comprehension of
EFL learners in an ESP course. The research employs ANCOVA to compare the performance of 60 students in
Iran. These students are divided into two groups; a group that receives NLP instruction and a control group
that follows the ESP reading approach. The findings reveal that the experimental group demonstrates reading
comprehension skills compared to the control group. This suggests that incorporating NLP techniques can
enhance EFL learner’s abilities when it comes to reading in ESP courses. The paper is well written and
effectively presents evidence supporting the effectiveness of NLP techniques in ESP reading. However there
are areas for improvement in this paper. These include expanding the sample size implementing
randomization techniques extending the duration of treatment considering factors that influence reading
comprehension providing details regarding how NLP was implemented and discussing the implications of
using NLP techniques on reading comprehension. Overall this research highlights how utilizing NLP
techniques can positively impact EFL learners reading skills in ESP courses while also suggesting areas, for
exploration and improvement.
Arulselvi, E. (2018). In this paper explores the theory of Multiple intelligences (MI) proposed by Howard
Gardner and its implications, for teaching English. The author gives an overview of the eight types of
intelligences and Gardner’s identification of five minds for the future. Various activities and tasks are
suggested to accommodate learning styles and preferences among language learners. The paper highlights the
significance of students recognizing their strengths and weaknesses well as creating a learner cantered and
authentic learning environment. It is an organized and informative paper that offers examples and resources
to teachers interested in incorporating MI theory into their classrooms. However it could benefit from
evidence supporting the effectiveness of MI based instruction as well as addressing challenges and
limitations, in implementing MI theory across different contexts.
Rayati, M. (2021). In this study the author explores the use of neuro linguistic programming (NLP)
techniques, in English language teaching (ELT) in Iran. The author conducted a 16 hour NLP training
workshop for a group of 20 EFL teachers, observed their teaching practices both before and after the
workshop. Additionally interviews were conducted with these teachers to gain insights into their perceptions
of NLP and its impact on their teaching methodologies. The findings revealed that the majority of teachers
incorporated NLP techniques to varying extents in their classes resulting in outcomes such as improved
rapport with students, flexibility, increased motivation and stimulated critical thinking skills. This paper
offers suggestions for EFL teachers, teacher educators and researchers interested in exploring the
implications of NLP for ELT. The paper is well written and well-structured making a contribution to existing
literature on NLP and ELT within EFL contexts. However it is worth noting that there are limitations that
could be further addressed in research endeavors. These include factors like the sample size used in this study
the convenience sampling method employed for participant selection incomplete learner perspectives
evaluation as well as the absence of a control group, for comparison purposes.
Zhang, X. et al. (2023). This research paper explores how neuro linguistic programming (NLP) impacts the
achievements, emotional intelligence (EI) and critical thinking abilities of advanced learners studying English
as a foreign language. The study followed a design conducting pre-tests and post-tests to compare the
progress of 50 learners who received NLP training, with a control group. The findings indicated that NLP had
an influence on all three variables suggesting that it can be a valuable technique for enhancing learning
outcomes and personal growth. The paper includes an in depth review of existing literature, an explanation of
the intervention method employed and a clear presentation of the data analysis. However there are some
Asiqur Rahaman / Kuey, 30(2), 1627 601
areas for improvement within the paper such as addressing limitations like the homogenous sample size, lack
of follow up assessments and potential factors that could impact motivation and self -regulation. Overall this
research contributes to the field of NLP and education by offering evidence supporting the effectiveness of
NLP strategies, in improving both abilities and emotional skills among learners.
Octaberlina, L. R., & Asrifan, A. (2021). In this paper the concept of multiple intelligences and its impact, on
learning in schools is explored. The authors provide an overview of Howard Gardner’s eight proposed types of
intelligence. Discuss how they can be practically applied, developed and evaluated in the classroom.
Additionally the paper offers examples of learning activities that cater to types of intelligence emphasizing the
importance for educators to acknowledge and appreciate their student’s unique strengths and potentials.
With its written and informative content this paper offers insights on how to enhance learning outcomes and
experiences for elementary school students. However it would benefit from evidence and research findings to
support the claims and recommendations put forth by the authors. Furthermore a clear conclusion
summarizing the points and implications of the paper would strengthen its impact. All this contribution
enriches existing literature, on multiple intelligences and education.
Arnold, J., & Fonseca, M. C. (2004). In this research paper we delve into the implications of Gardner’s theory
of Multiple Intelligences Theory (MIT), on the learning and teaching of languages. The authors argue that
MIT offers a perspective that acknowledges and supports the range of learners providing various methods to
engage with meaning and memory. The paper examines the elements of MIT and how it relates to learning
styles, motivation, evaluating stimuli, language aptitude, personal relevance and memory. It also suggests
ways in which different intelligences can be utilized as frameworks, for language instruction by presenting
examples of activities that cater to musical visual spatial logical mathematical bodily kinesthetic,
interpersonal, intrapersonal and naturalist intelligences. Ultimately the paper concludes that MIT not
enhances communicative skills but also fosters personal growth and social development within language
classrooms.
Nicholson-Nelson, K. (1998). In this book offers an exploration of how to develop students multiple
intelligences (MI) within the classroom. As a teacher she shares insights and practical strategies, for
implementing MI across different subjects and grade levels. The book is filled with engaging examples of
student projects, assessment tools and classroom activities that effectively incorporate MI principles.
Nicholson Nelson’s enthusiasm and expertise in MI shine through the organized and informative content. It's
worth noting that her book, "Developing Students Multiple Intelligences” serves as a resource for teachers
aiming to enhance their students learning and motivation. However it would be beneficial if the book
included references to support the author’s claims and arguments. For instance citing research studies and
literature reviews that explore the effectiveness and challenges of MI in education would strengthen the
credibility of the book. Additionally addressing criticisms surrounding MI theory—such as a lack of evidence
or difficulties in measuring and assessing MI—along with concerns about stereotyping or labeling based on
students MI profiles would provide a more balanced perspective on its implications, for teaching and
learning.
Tahiri, Z. (2023). In this research paper we delve into Howard Gardner’s theory of Multiple Intelligences (MI)
and its implications, for English as a language (EFL) classrooms in Kosovo. The study employed a
combination of questionnaires and classroom observations to examine how familiar EFL teachers were with
MI theory how they implemented it in their teaching and the correlation between the theory and their
teaching strategies. The findings indicated that while most EFL teachers had an understanding of MI theory
they struggled to apply it in their classrooms. Furthermore the study revealed that the majority of teachers
possessed intelligence profiles and predominantly relied on teacher cantered and behaviorist teaching
methods. To address this issue the author proposed that EFL teachers should adopt a student cantered
approach that caters to the needs and abilities of learners. Overall this paper offers an overview of MI theory’s
relevance in EFL teaching and learning contexts. However certain limitations need attention, such as the
sample size, absence of a control group and potential bias in self-reported data. Additionally further analysis
is warranted concerning classroom observations and exploring connections between teachers’ intelligence
profiles and their chosen teaching strategies. It would also be beneficial to provide recommendations along,
with examples showcasing how MI theory can be integrated effectively into EFL classrooms.
Massanet Oliver, A. (2017). In this thesis the author delves into the significance of motivation and multiple
intelligences, in English as a Foreign Language (EFL) classroom drawing on Howard Gardner’s theory of
intelligences. The author presents a unit and a project that aim to engage students with various learning
styles and abilities through different types of activities. Additionally the author conducts a survey among
bachillerato students to examine their motivation levels and understand their intelligences. The thesis
includes a literature review on intelligence, motivation and multiple intelligences along with examples of
their application in an EFL context. It is well organized, clear and informative while offering insights, for EFL
teachers and learners. However further empirical evidence could strengthen the effectiveness of the proposed
unit and project. Moreover it would be beneficial to discuss the challenges and limitations associated with
implementing the intelligences approach in classrooms. In addition conducting an analysis of both survey
results and student feedback would enhance the quality of this thesis.
4. Research questions:
602 Asiqur Rahaman / Kuey, 30(2), 1627
I. How can the combination of Neuro Linguistic Programming (NLP) and Multiple Intelligences (MI)
improve the process of learning English as a language?
II. What are the benefits and difficulties that arise when integrating NLP and MI, in language
education?
III. In what ways can utilize both NLP and MI help connect theory with application in language learning?
IV. How might the integration of NLP and MI impact learning styles and abilities, in language
education?
5. Research objectives:
I. To explore the advantages of combining Neuro Linguistic Programming (NLP) with Multiple
Intelligences (MI) in language learning.
II. To review the body of literature on NLP and MI focusing on the potential and difficulties of integrating
them into language education.
III. To showcase how harnessing the power of NLP and MI can create learning environments that cater to
learning styles and abilities.
IV. To bridge the gap, between theory and practice, in language learning by integrating NLP and MI.
6. Theoretical framework
The theoretical framework primarily relies on the neuro theory, which focuses on how information's
processed through visual, auditory and kinesthetic modes (VAK). This researches theoretical foundation
involves comprehending the principles and applications of Neuro Linguistic Programming (NLP) and
Multiple Intelligences (MI) in the context of language acquisition for English as a language. It encompasses
exploring NLPs approach, to enhancing communication, behavior and strategic thinking along with
Gardner’s theory of intelligences and its implications, for language learning.
7. Research Method and Design
The research used SPSS software to analyses the data, which allowed them to test hypotheses and explore
relationships, between variables. A mixed methods approach which involved observing, interviewing and
surveying participants as conducting ANCOVA analysis. Additionally the administered questionnaires and
observed classrooms to gauge the motivation levels of bachillerato students and gain insights into their
intelligences. A combination of questionnaires and classroom observations to assess how familiar EFL
teachers are with MI theory how they incorporated it into their teaching practices and whether there is a
correlation, between the theory and their teaching strategies.
8. Results and Discussion
The frequency table, Table 1 provides a breakdown of the number and percentage of cases, for each value.
For example let’s take a look at the Freq_Intel. We have 2 cases (6.3%) with a value of 1, 1 case (3.1%) with a
value of 2 and so on. The cumulative percent helps us understand the percentage of cases with a value to or
less than the value. For instance when it comes to Freq_Intel we find that 6.3% of cases have a value less than
or equal to 1 while 9.4% have a value than or equal to 2.
The distribution of each variable and compare the frequencies and percentages across the values. It's also
worth looking for any patterns, trends, outliers or anomalies in the data that may catch your attention. Here
are some examples;
The majority of cases show a frequency (value = 4) when it comes to activities (Freq_Intel). Few cases have a
low frequency (value = 2).
Cooperative or individual learning methods (Pref_Method) tend to be preferred by cases with values of
either 4 or 5, as their common choices. On the hand competitive methods are preferred by very few cases with
a value of 3.
Keep these points in mind while interpreting the table and exploring its implications.
The majority of people tend to have a level of enjoyment (usually rated as 5) when it comes to engaging in
activities. However there are a few cases where individuals express a level of enjoyment (rated as 3). This
pattern can be observed across variables well.
Table -1
Asiqur Rahaman / Kuey, 30(2), 1627 603
Frequencies
Statistics
Freq_In
tel
Pref_Meth
od
Enjoy_Cr
eat
Comp_Rec
all
React_O
bs
Motiv_C
onf
Goal_Mo
nit
Freq_R
efl
Benefit_Co
op
Respect_
Div
N
Valid
32
32
32
32
32
32
32
32
32
32
Missi
ng
0
0
0
0
0
0
0
0
0
0
Frequency Table
Freq_Intel
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1
2
6.3
6.3
6.3
2
1
3.1
3.1
9.4
3
4
12.5
12.5
21.9
4
22
68.8
68.8
90.6
5
3
9.4
9.4
100.0
Total
32
100.0
100.0
Pref_Method
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
3
1
3.1
3.1
3.1
4
16
50.0
50.0
53.1
5
15
46.9
46.9
100.0
Total
32
100.0
100.0
Enjoy_Creat
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
3
4
12.5
12.5
12.5
4
12
37.5
37.5
50.0
5
16
50.0
50.0
100.0
Total
32
100.0
100.0
Comp_Recall
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1
1
3.1
3.1
3.1
2
1
3.1
3.1
6.3
3
7
21.9
21.9
28.1
4
13
40.6
40.6
68.8
5
10
31.3
31.3
100.0
Total
32
100.0
100.0
React_Obs
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
2
1
3.1
3.1
3.1
3
6
18.8
18.8
21.9
4
10
31.3
31.3
53.1
5
15
46.9
46.9
100.0
Total
32
100.0
100.0
Motiv_Conf
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
4
6
18.8
18.8
18.8
5
26
81.3
81.3
100.0
Total
32
100.0
100.0
Goal_Monit
604 Asiqur Rahaman / Kuey, 30(2), 1627
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1
1
3.1
3.1
3.1
2
2
6.3
6.3
9.4
3
6
18.8
18.8
28.1
4
16
50.0
50.0
78.1
5
7
21.9
21.9
100.0
Total
32
100.0
100.0
Freq_Refl
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1
1
3.1
3.1
3.1
2
3
9.4
9.4
12.5
3
8
25.0
25.0
37.5
4
15
46.9
46.9
84.4
5
5
15.6
15.6
100.0
Total
32
100.0
100.0
Benefit_Coop
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
2
2
6.3
6.3
6.3
3
6
18.8
18.8
25.0
4
14
43.8
43.8
68.8
5
10
31.3
31.3
100.0
Total
32
100.0
100.0
Respect_Div
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
1
2
6.3
6.3
6.3
3
3
9.4
9.4
15.6
4
12
37.5
37.5
53.1
5
15
46.9
46.9
100.0
Total
32
100.0
100.0
The first analysis, called "Table -2 " involved comparing the frequency of intelligence related activities,
between males and females using an independent samples t test. The results indicated that there was no
difference in how males and females engaged in these activities. In words both genders showed levels of
involvement. The observed difference between the groups was not very large falling within a to medium effect
size range.
Next in the analysis we conducted a paired samples t test to compare three sets of variables; enjoyment of
activities and comprehension recall reaction to observation and preferred method of learning and age and
goal monitoring. The findings revealed that there was a distinction between enjoyment of activities and
comprehension recall. This suggests that participants who reported levels of enjoyment in activities also
displayed better comprehension recall scores. This difference was considered substantial as it yielded an
effect size.
However no significant distinction emerged between reaction to observation and preferred method of
learning. In terms participants who reacted strongly to observations did not display a preference for specific
learning methods, over others. The observed difference fell under the small effect size category indicating that
it was relatively insignificant.
Lastly there was a difference when comparing age and goal monitoring scores. Older participants tended to
have goal monitoring scores compared to their counterparts.
The magnitude of the effect was substantial suggesting that the difference was significant.
Table-2
T-Test
Group Statistics
Asiqur Rahaman / Kuey, 30(2), 1627 605
Gender
N
Mean
Std. Deviation
Std. Error Mean
Freq_Intel
Male
13
3.92
.641
.178
Female
19
3.58
1.071
.246
Independent Samples Test
Levene's Test
for Equality of
Variances
t-test for Equality of Means
F
Sig.
t
df
Significance
Mean
Difference
Std. Error
Difference
95% Confidence
Interval of the
Difference
One-
Sided
p
Two-
Sided
p
Lower
Upper
Freq_Intel
Equal
variances
assumed
4.408
.044
1.036
30
.154
.309
.344
.332
-.334
1.023
Equal
variances not
assumed
1.135
29.606
.133
.265
.344
.303
-.275
.964
Independent Samples Effect Sizes
Standardizera
Point Estimate
95% Confidence Interval
Lower
Upper
Freq_Intel
Cohen's d
.923
.373
-.342
1.082
Hedges' correction
.947
.363
-.333
1.054
Glass's delta
1.071
.321
-.396
1.030
a. The denominator used in estimating the effect sizes.
Cohen's d uses the pooled standard deviation.
Hedges' correction uses the pooled standard deviation, plus a correction factor.
Glass's delta uses the sample standard deviation of the control group.
T-Tes
Paired Samples Statistics
Mean
N
Std. Deviation
Std. Error Mean
Pair 1
Enjoy_Creat
4.38
32
.707
.125
Comp_Recall
3.94
32
.982
.174
Pair 2
React_Obs
4.22
32
.870
.154
Pref_Method
4.44
32
.564
.100
Pair 3
Age
16.66
32
.545
.096
Goal_Monit
3.81
32
.965
.171
Paired Samples Correlations
N
Correlation
Significance
One-Sided p
Two-Sided p
Pair 1
Enjoy_Creat & Comp_Recall
32
.081
.329
.658
Pair 2
React_Obs & Pref_Method
32
.193
.145
.290
Pair 3
Age & Goal_Monit
32
-.372
.018
.036
Paired Samples Test
Paired Differences
t
df
Significance
Mean
Std.
Deviation
Std.
Error
Mean
95% Confidence
Interval of the
Difference
One-
Sided p
Two-
Sided p
Lower
Upper
Pair
1
Enjoy_Creat -
Comp_Recall
.438
1.162
.205
.018
.857
2.129
31
.021
.041
Pair
2
React_Obs -
Pref_Method
-.219
.941
.166
-.558
.121
-1.315
31
.099
.198
Pair
3
Age - Goal_Monit
12.844
1.273
.225
12.385
13.303
57.084
31
<.001
<.001
Paired Samples Effect Sizes
606 Asiqur Rahaman / Kuey, 30(2), 1627
Standardizera
Point
Estimate
95% Confidence
Interval
Lower
Upper
Pair 1
Enjoy_Creat -
Comp_Recall
Cohen's d
1.162
.376
.015
.732
Hedges'
correction
1.191
.367
.014
.715
Pair 2
React_Obs -
Pref_Method
Cohen's d
.941
-.232
-.582
.121
Hedges'
correction
.965
-.227
-.568
.118
Pair 3
Age - Goal_Monit
Cohen's d
1.273
10.091
7.565
12.610
Hedges'
correction
1.305
9.845
7.380
12.302
a.The denominator used in estimating the effect sizes.
Cohen's d uses the sample standard deviation of the mean difference.
Hedges' correction uses the sample standard deviation of the mean difference, plus a correction factor.
The first table, Table 3 presents the connection, between gender and preferred learning methods. The
findings indicate that there is no preference for a method among males or females. Both genders exhibit
proportions of auditory learners. However females have a likelihood of being kinesthetic learners compared
to males (47.4% vs 30.8%).
The second table demonstrates the relationship between class and preferred learning methods. The results
reveal a disparity in preferences across the three classes. Class 1 has the proportion of learners (50%) whereas
class 2 has the lowest (16.7%). Class 3 possesses the proportion of learners (58.3%) while class 1 has the
lowest (25%). Additionally class 2 displays the percentage of learners (66.7%), with class 3 having the lowest
percentage (16.7%). These outcomes imply that different classes may exhibit learning styles and
requirements.
Table- 3
Crosstabs
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Gender * Pref_Method
32
100.0%
0
0.0%
32
100.0%
Crosstabs
Case Processing Summary
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Class * Pref_Method
32
100.0%
0
0.0%
32
100.0%
Asiqur Rahaman / Kuey, 30(2), 1627 607
The results of the regression analysis (Table 4) display the outcomes of fitting a model to forecast the
Comp_Recall (composite recall score) by utilizing five predictor variables; Freq_Intel (frequency of engaging
in intellectual activities) Pref_Method (preference, for learning methods) Enjoy_Creat (enjoyment of creative
activities) React_Obs (reaction to observations) and Motiv_Conf (motivation and confidence). The model
accounts for 37% of the variability in Comp_Recall, which is considered an amount. Furthermore the model
exhibits significance at the 0.05 level indicating that it is unlikely to have occurred. Among these predictor
variables only Freq_Intel has an impact on Comp_Recall with a coefficient of 0.443. This implies that with
every one unit increase in Freq_Intel we can expect Comp_Recall to increase by 0.443 units while keeping
variables constant. On the hand the remaining four predictor variables demonstrate effects on Comp_Recall
as their coefficients are close to zero. Lastly it is worth noting that the constant term value of 0.977 represents
the predicted Comp_Recall value when all predictor variables are set to zero; however its relevance, within
this context is limited.
Table-4
Regression
Variables Entered/Removeda
Model
Variables Entered
Variables Removed
Method
1
Motiv_Conf, Enjoy_Creat, Pref_Method, Freq_Intel, React_Obsb
.
Enter
a. Dependent Variable: Comp_Recall
b. All requested variables entered.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.609a
.371
.250
.850
a. Predictors: (Constant), Motiv_Conf, Enjoy_Creat, Pref_Method, Freq_Intel, React_Obs
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
11.072
5
2.214
3.062
.026b
Residual
18.803
26
.723
Total
29.875
31
a. Dependent Variable: Comp_Recall
b. Predictors: (Constant), Motiv_Conf, Enjoy_Creat, Pref_Method, Freq_Intel, React_Obs
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
.977
2.350
.416
.681
Freq_Intel
.443
.187
.417
2.373
.025
Pref_Method
.337
.279
.194
1.208
.238
Enjoy_Creat
-.023
.224
-.017
-.103
.919
React_Obs
.309
.206
.274
1.503
.145
608 Asiqur Rahaman / Kuey, 30(2), 1627
Motiv_Conf
-.288
.429
-.116
-.672
.508
The analysis, in Table 5 factor analysis on 12 variables using the principal component method without
rotation. From this analysis three components were extracted, explaining 61.3% of the variance in the data.
The first component showed relationships with all variables except Pref_Method, Enjoy_Creat and
Benefit_Coop which suggests it represents a general factor related to performance or competence. The
second component had associations with Pref_Method, Motiv_Conf, Goal_Monit and Freq_Refl indicating it
captures a factor connected to self-regulation or metacognition. Lastly the third component had connections
with Enjoy_Creat, Goal_Monit and Respect_Div implying it reflects a factor relating to creativity or diversity.
The communalities ranged from 0.429 to 0.744; this indicates that some variables are better represented by
the components, than others. The scree plot clearly shows a break after the third component was extracted
and supports our decision to choose three components.
Table- 5
Factor Analysis
Communalities
Initial
Extraction
Freq_Intel
1.000
.739
Pref_Method
1.000
.429
Enjoy_Creat
1.000
.522
Comp_Recall
1.000
.546
React_Obs
1.000
.663
Motiv_Conf
1.000
.586
Goal_Monit
1.000
.744
Freq_Refl
1.000
.604
Benefit_Coop
1.000
.663
Respect_Div
1.000
.632
Extraction Method: Principal Component
Analysis.
Total Variance Explained
Component
Initial Eigenvalues
Extraction Sums of Squared Loadings
Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
1
2.996
29.959
29.959
2.996
29.959
29.959
2
1.678
16.777
46.736
1.678
16.777
46.736
3
1.455
14.550
61.286
1.455
14.550
61.286
4
.960
9.602
70.888
5
.806
8.063
78.951
6
.629
6.290
85.241
7
.578
5.784
91.025
8
.398
3.984
95.009
9
.290
2.895
97.904
10
.210
2.096
100.000
Extraction Method: Principal Component Analysis.
Component Matrixa
Component
1
2
3
Freq_Intel
.802
-.303
-.056
Pref_Method
.416
.452
.227
Enjoy_Creat
.309
-.249
.604
Comp_Recall
.669
-.228
-.217
React_Obs
.580
.008
-.571
Motiv_Conf
.445
.382
-.493
Goal_Monit
.323
.632
.490
Freq_Refl
.385
.668
-.096
Benefit_Coop
.615
-.523
.104
Respect_Div
.686
-.043
.399
Extraction Method: Principal Component Analysis.
a. 3 components extracted.
Asiqur Rahaman / Kuey, 30(2), 1627 609
The cluster analysis, in Table 6 employed the linkage method. Squared Euclidean distance measure to group
32 cases based on 10 variables associated with learning styles. The results yielded a tree diagram that
illustrates the similarities between the cases and clusters. Based on the analysis it appears that there are three
clusters with sub clusters within them.
The first cluster comprises cases 1, 2 3 4 6 10, 11 12,13,14,15,16,17,18,19,20 21,23,24,25,26,27,28,29 30 and32.
This particular cluster exhibits a coefficient of 15.778 which indicates similarity among these cases. On the
hand the second cluster encompasses cases5,7,8 and9.This cluster demonstrates a coefficient of 24.655
indicating relatively lower similarity among these cases. Lastly the third cluster solely consists of case22
which is considered an outlier due to its coefficient of47.355.This implies that case22 has very little similarity,
with any other case. By utilizing cluster analysis techniques it becomes possible to identify learning styles
among these cases and subsequently customizes interventions accordingly.
Table- 6
Cluster
Case Processing Summarya,b
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
32
100.0
0
.0
32
100.0
a. Squared Euclidean Distance used
b. Average Linkage (Between Groups)
Average Linkage (Between Groups)
Agglomeration Schedule
Stage
Cluster Combined
Coefficients
Stage Cluster First Appears
Next Stage
Cluster 1
Cluster 2
Cluster 1
Cluster 2
1
18
30
2.000
0
0
9
2
9
29
3.000
0
0
8
3
4
28
3.000
0
0
21
4
20
27
3.000
0
0
10
5
21
23
3.000
0
0
7
6
2
5
3.000
0
0
11
7
17
21
3.500
0
5
11
8
9
19
3.500
2
0
15
9
16
18
4.000
0
1
18
10
20
24
4.500
4
0
12
11
2
17
4.500
6
7
13
12
11
20
4.667
0
10
17
13
2
15
4.800
11
0
19
14
13
31
5.000
0
0
16
15
9
25
5.333
8
0
19
16
12
13
5.500
0
14
23
17
10
11
5.500
0
12
21
18
3
16
6.000
0
9
20
19
2
9
6.250
13
15
20
20
2
3
6.750
19
18
23
21
4
10
7.100
3
17
22
22
4
6
8.857
21
0
24
23
2
12
9.286
20
16
24
24
2
4
10.426
23
22
26
25
22
26
12.000
0
0
26
26
2
22
15.000
24
25
27
27
2
14
15.778
26
0
28
28
2
8
17.714
27
0
29
29
2
7
24.655
28
0
30
30
2
32
28.700
29
0
31
31
1
2
47.355
0
30
0
610 Asiqur Rahaman / Kuey, 30(2), 1627
.9. Conclusion and Recommendations
Conclusion:
The research highlights the advantages of combining Neuro Linguistic Programming (NLP) and Multiple
Intelligences (MI) in language learning. By integrating these two approaches we can create learning
environments that cater to learning styles and abilities. The study employed SPSS software, for data analysis
allowing for hypothesis testing and exploration of relationships between variables. The results indicate that
incorporating NLP and MI into language learning holds promise for addressing differences and preferences
during the learning process while also improving outcomes for both learners and educators. Ultimately this
study suggests that integrating NLP and MI can contribute to an English language learning environment.
Recommendations:
Based on the findings it is recommended that educators consider integrating NLP and MI principles into their
language teaching practices. This integration can help establish learning experiences that accommodate
individual learning styles and preferences. Moreover educators should be mindful of the intelligences and
modalities among their students adapting their teaching methods accordingly. Additionally further research
is encouraged to explore the application of NLP and MI, in language education. It would also be beneficial to
develop training programs for educators to effectively utilize these approaches in their teaching.
The combination of NLP and MI holds promise in connecting aspects with applications, in language learning
thereby creating a more inclusive and efficient learning atmosphere for students with different learning
preferences and capabilities.
These findings and suggestions stem from an analysis of existing literature, on NLP and MI emphasizing the
opportunities and obstacles linked to incorporating them into language education.
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