See discussions, stats, and author profiles for this publication at: https://www.researchgate.

net/publication/325128370

Effect of Motivation, Learning Style and Discipline Learn about Academic

Achievement Additional Mathematics

Article in International Journal of Academic Research in Business and Social Sciences · April 2018
DOI: 10.6007/IJARBSS/v8-i4/4059

CITATIONS READS

13 2,807

2 authors, including:

Abdul Hakim Abdullah

Universiti Sultan Zainal Abidin | UniSZA
98 PUBLICATIONS 225 CITATIONS

SEE PROFILE

All content following this page was uploaded by Abdul Hakim Abdullah on 14 May 2018.

The user has requested enhancement of the downloaded file.

Effect of Motivation, Learning Style and Discipline Learn
about Academic Achievement Additional Mathematics
Zamri Chik, Abdul Hakim Abdullah
To Link this Article: http://dx.doi.org/10.6007/IJARBSS/v8-i4/4059 DOI: 10.6007/IJARBSS/v8-i4/4059

Received: 23 Feb 2018, Revised: 16 Mar 2018, Accepted: 23 April 2018

Published Online: 26 April 2018

In-Text Citation: (Chik & Abdullah, 2018)

To Cite this Article: Chik, Z., & Abdullah, A. H. (2018). Effect of Motivation, Learning Style and Discipline Learn
about Academic Achievement Additional Mathematics. International Journal of Academic Research in
Business and Social Sciences, 8(4), 772–787.

Copyright: © 2018 The Author(s)

Published by Human Resource Management Academic Research Society (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute,
translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full
attribution to the original publication and authors. The full terms of this license may be seen
at: http://creativecommons.org/licences/by/4.0/legalcode

Vol. 8, No. 4, April 2018, Pg. 772 - 787

http://hrmars.com/index.php/pages/detail/IJARBSS JOURNAL HOMEPAGE

Full Terms & Conditions of access and use can be found at

http://hrmars.com/index.php/pages/detail/publication-ethics
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

Effect of Motivation, Learning Style and Discipline

Learn about Academic Achievement Additional
Mathematics
Zamri Chik, Abdul Hakim Abdullah
Faculty of Islamic Contemporary Studies, Universiti Sultan Zainal Abidin, Gong Badak Campus,
21300 Kuala Terengganu, Terengganu, Malaysia

Abstract
Using quantitative research methods based on Structural Equation Modeling (SEM) in
educational research, to analyze the various relationships among variables in the model formed
based on the theories studied, few researchers did. This study was conducted to determine the
effectiveness of motivation, learning style and discipline of teach on academic achievement Form
four students Additional Mathematics in Kuala Terengganu District. The instrument used in this
study is based on the School Inventory Learning model developed by Selmes (1987). The item
questionnaire in this instrument has been adapted to the investigation investigation. A total of
260 research samples were included in the study, consisting of four forms students in 10 schools
in Kuala Terengganu District. Data were analyzed using IBM-SPSS-AMOS (SEM) program version
21.0. SEM analysis consists of two main models: the measurement model and the Structural
model. Prior to the SEM test, some adjustment tests were performed to ensure that the tested
indicator actually represented the measured construct. Two analyzes in this study are
prerequisites that have been met before the SEM analysis is done ie Factor Exploration Analysis
(EFA) and Confirmatory Factor Analysis (CFA). The findings indicate that the motivation, learning
style and discipline of learning have a positive and significant effect on student achievement in
academic achievement. Furthermore, motivation also has a positive and significant impact on the
learning discipline, but the learning style has no positive and not significant effect on the learning
discipline. Intermediate analysis findings for the learning discipline take place between
motivation and academic achievement and do not occur between learning styles and academic
achievement. The findings in this study indicate that educators need to instill enthusiasm for
students as well as to know their students' learning styles and to ensure that students have a
learning discipline, because it can affect student academic achievement.
Keywords: Structural Equation Modeling (SEM), Motivation, Learning Styles, Learning Discipline.

773
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

Introduction
Education is a constantly changing field in line with the development of the environment. These
changes affect education especially in the curriculum aspect. To make Malaysia a developed
country by 2020, the field of education has been identified as one of the key success factors
(critical success factors). The Malaysian Government through the Ministry of Education of
Malaysia (MOE) has always designed, planned and improved the education system in Malaysia.
Among the steps taken include the introduction of the Education Development Master Plan
(PIPP, 2006-2010) and the latest Malaysian Education Development Plan (2013-2025) as wages
and at the same time leading to the transformation of national education. 21st Century learning
is a world education transformation based on a more dynamic and creative approach to learning
and facilitation (PdPc) with relevant learning content in line with current developments. Teachers
must be prepared to accept change and manage change efficiently and effectively as they are the
implementing group responsible for implementing the change. Teachers act as planners, careers,
counselors, drivers and assessors (Malaysian Quality Standard of Education Wave 2-SCKMg2) to
develop the full potential of students to produce student academic achievement continuously at
an optimal level.

Education is a constantly changing field in line with the development of the environment.
During the PdPc process at school, teachers are the main factors that can influence the way
students learn. Although some students learn something according to their own approach or
method, they do not realize that the method they use is a distinctive and different learning style
with other students. According to Emeliana et al. (2012), teachers should make full use of every
learning style to make learning more interesting. Teachers should also communicate clearly,
motivate and apply flexible learning styles, especially in the Supplemental Mathematics lessons
that are mostly taught in schools. Based on the theory of motivation by using goal-setting theory,
the main goal of achieving a person influences achievement through variation in the quality of
self-regulatory processes (Locke, 2005). This self-regulation process is closely related to a
student's metacognitive abilities or skills. It shows an indirect relationship between motivation
and academic achievement through metacognition. Students need the enthusiasm and
motivation as well as an effective way of learning to overcome their weaknesses in Supplemental
Mathematics. Therefore, this study will look at the role played by motivation (internal and
external) towards students to achieve additional academic mathematics achievement.

Various teaching methods have been used in schools aimed at improving the academic
achievement of the students' Additional Mathematics subjects, to ensure decline and problems
arising in additional mathematics learning can be identified. In addition to the students' own
factors that lead to a decrease in performance in the Mathematics Supplementary subjects,
educators also sometimes have no suggestions or motivations for their students. Some even
consider that a weak student is a habit or an ordinary trait, without trying to give advice or to
overcome it. Some weak and self-aware students sometimes exist, but the need for appropriate
and effective motivation and encouragement and learning styles of educators is essential.
The importance of teachers to know and understand a student's learning style is because
the effectiveness of a student's learning style may not be the same. Thus, teachers need to
introduce different learning styles to ensure the appropriateness of all students involved.

774
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

Students also need to know which learning styles are appropriate for them, while teachers need
to play an important role in helping their students understand the trends and ways they learn, to
improve the effectiveness of learning so as to achieve good results. Several findings have been
made in the West, finding the suitability and motivation of motivation with learning styles can
produce good academic achievement. According to Nelson (2003), there is a positive impact
between motivation and learning styles on student achievement. Students exposed to learning
styles and motivated, achieving higher academic achievement, compared with those not
exposed. During the PdPc process, teachers must diversify teaching strategies to create positive
stimuli for students to learn. In this way, teachers will be able to increase students' interest and
curiosity towards their teaching. Students who are motivated by teachers will usually be more
interested in helping the process of achieving learning goals.

The purpose of this study was to examine the effectiveness of motivation, learning styles
and discipline of learning on student academic achievement, as well as the role played by the
discipline of learning as a mediator of the relationship between motivation and learning styles to
the academic achievement of four students.

Research Methodology
The research method used is quantitative, and using research instruments based on the Learning
Inventory model in the School developed by Selmes (1987). The questionnaire items have been
adjusted according to the suitability of the learning system in SMA. Data were analyzed using
Structural Equation Modeling (SEM) with IBM-SPSS-AMOS program version 21.0. SEM is formed
with two main models namely measurement model and Structural model. Before the SEM test is
tested, prior adjustment tests should be made to ensure that the tested indicator actually
represents the measured construction. There are two analyzes as prerequisites that must be met
before the SEM analysis is performed: (1) Exploration Analysis Factor (EFA), and (2) Confirmation
Factor Analysis (CFA). Validation factor analysis (CFA) is a test of measurement model to ensure
that each construction meets procedures such as validity and reliability for each experiment
being built (Kline, 2016; Awang, 2015; Chua, 2014d; Byrne; 2013; Hair et al., 2006; Schumucker
& Lomax, 2004). Comparison of model measurement is essential to ensure that any latent
construction in this study is compatible with the data studied before SEM can be continued (Kline,
2016; Awang, 2015; Schumucker & Lomax, 2004).

Using the CFA method can assess the extent to which factors are observed significantly to
the latent construction used. This assessment is done by examining the stiffness value of the
regression pathway from factor to observed variable (factor loading) rather than the relationship
between factors (Byrne, 2001). Through the use of CFA, any item not conforming to the
measurement model is derived from the model. This inequality is due to the low load factor value.
Researchers need to apply the CFA process to all model-related constructions, either separately
or collectively (combined CFA models) (Alias & Hartini, 2017).

The compatibility of the hypothetical models tested is verified using the Fitness Indexes
to see the values of Root Mean Square Error of Approximation (RMSEA<0.08), Goodness of Fit
Index (GFI>0.90), Comparative Fit Index (CFI>0.90) and Chi Square/Degree of Freedom (chisq/df

775
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

<5.0). According to Hair et al. (2006) if the value of χ2 is less than 2.00 but significant, it should
be noted whether the sample is large or vice versa. Sample size above 200 can cause χ2 to be
significant. Therefore, Hair and his colleagues propose two other indices namely CFI and RMSEA
to ensure CFA analysis establishes a dimensionless research model. If the CFI value exceeds 0.90
and the RMSEA is less than 0.08 it is said that the existence of Unidimensionality exists for the
formation of each construct.

The hypothetical model is considered to be in accordance with the research data when
the chisq/df value is less than 3.0 (Marsh and Hocevar, 1985). The hypothetical model is also
considered to correspond to a GFI value greater than 0.90 (Joreskog and Sorbom, 1993). The
value of RMSEA is very good if it is smaller than 0.08 (Hair et al., 2006; Browne & Cudeck, 1993),
but still less than 0.1 (Byrne, 1998, 2013). Bentler (1990) also recommends receiving CFIs over
0.90. But the CFI value between 0.80 and 0.89 is still at the margin received. To verify the model
developed, the boostrapping value is determined. According to Bollen & Stine (1992), the
developed model is considered to have validity when the bootstrap value exceeds 0.05 means
there is no difference between the data collected from the sample with the proposed model.
Therefore, the proposed model is valid based on data collected from the research sample.

Research Findings
CFA Analysis for Conventional Motivation Measurement Models

The Analysis of Fitness Index in Table 1 shows the Motivation Construction Model has reached
the level of Compatibility Index level. This means Building Validity for this construction has been
achieved (Awang 2011; 2012; 2014; 2015; Awang et al., 2015a; Kashif et al., 2016).

Table 1: Analysis to Determine Construct Validity

Name Category Name Index Index value Research Findings
1. Absolute fit RMSEA 0.074 Reached the set level
2. Incremental fit CFI 0.975 Reached the set level
3. Parsimonious fit ChiSq/df 2.309 Reached the set level

The Measurement Model for the construction of Motivation has reached the level of
Compatibility Index. This means Building Validity for this construction has been achieved (Awang
2011; 2012; 2014; 2015; Awang et al., 2015a; Kashif et al., 2016).

776
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

Figure 1: Measurement Model for the Second Layout of Building Motivation

CFA Analysis for Learning Styles Conflict Measurement Models

The Analysis of the Fitness Index in Table 2 shows the Learning Style Styles Measurement Model
has reached the level of Compatibility Level. This means Building Validity for this construction has
been achieved (Awang 2011; 2012; 2014; 2015; Awang et al., 2015a; Kashif et al., 2016). The
Measurement Model for Learning Style constructs has reached the level of Compatibility Index
level. This means Building Validity for this construction has been achieved (Awang 2011; 2012;
2014; 2015; Awang et al., 2015a; Kashif et al., 2016).

Figure 2: Second Layout Measurement Model for Learning Styles Build

Table 2: Analysis to Determine Construct Validity

Name Category Name Index Index value Research Findings
1. Absolute fit RMSEA 0.075 Reached the set level
2. Incremental fit CFI 0.919 Reached the set level
3. Parsimonious fit ChiSq/df 2.446 Reached the set level

777
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

CFA Analysis for Learning Discipline Model

The Analysis of Fitness Index in Table 3 shows Measurements of Constructive Model Learning
Discipline has reached the level of Compatibility Level. This means Building Validity for this
construction has been achieved (Awang 2011; 2012; 2014; 2015; Awang et al., 2015a).

Figure 3: Second Layout Measurement Models of Learning Discipline

Table 3: Analysis to Determine Construct Validity

Name Category Name Index Index value Research Findings
1. Absolute fit RMSEA 0.072 Reached the set level
2. Incremental fit CFI 0.995 Reached the set level
3. Parsimonious fit ChiSq/df 2.204 Reached the set level

The Measurement Model for the construction of the Learning Discipline has reached the
level of Compatibility Index. This means Building Validity for this construct has been achieved
(Awang 2011; 2012; 2014; 2015; Awang et al., 2015a; Kashif et al., 2016).

Confirmation Factor Analysis of All Measurement Models (Pooled CFAs)

The Integrated Validation Factor (CFA) analysis is required to evaluate the correlation value
between construct in the Discriminant Validity procedure. If the correlation value between
constructs exceeds 0.85, both constructs are said to be excessive (Awang, 2015; Hoque et al.,
2017; Awang et al., 2015a; Kashif et al., 2016). For overly complex models involving second-order
construction, joint validation factor analysis is difficult. Second level construction is a construct
that has dimensions or substructures where each dimension or substructure has a certain
number of items. The researcher will find it difficult to combine all the second level constructs in
one model to conduct Pooled Confirmatory Factor Analysis.

778
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

To solve this problem, all second order constructions need to be summarized into first
order construction by taking minutes from each sub-construction or dimension (Awang; 2014;
2015; Hoque et al., 2017). The procedural findings of Combined Factor Confirmation (Pooled CFA)
are shown in Figure 4. As always, the value on a single-headed arrow is the weighting factor of
each item, while the value on the double-headed arrow is the correlation between the
constructs. Through the Combined Validity Factor Analysis method, only one model of the
compatibility index represents all the constructed constructs. The findings from Table 4 show the
three categories of model compatibility indexes for all construction model constructions have
been achieved.

Table 4: Comparison of Value Index Models for Three Compatibility Categories

Name Category Name Index Index value Research Findings
1. Absolute fit RMSEA 0.066 Reached the set level
2. Incremental fit CFI 0.904 Reached the set level
3. Parsimonious fit ChiSq/df 2.128 Reached the set level

Another requirement of the validity that all constructs in the model need is Discrimination
Validity. Discriminatory validity is necessary to prove that all constructs in the model do not have
a strong relationship with each other causing multicollinearity problems (Awang, 2014; Hoque et
al., 2017; Awang et al., 2015a; Kashif et al., 2016). This verification requires researchers to
develop the Discrimination Index Validity Summary table. Table 6 shows the Summary of
Discrimination Validity Index among all constructs in the model.

Figure 4: Findings of Results from the CFA Construct Combinations Procedure

(Pooled Construct CFA)

Table 5: Summary of Discrimination Validity Index

Construct Motivation Learning Styles Learning Discipline
Motivation 0.865
Learning Styles 0.540 0.849
Learning Discipline 0.640 0.370 0.744

779
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

Table 5 presents the root values of the Index of Concentration Validity (AVE) for each
construct on the diagonal matrix. Another value in the table is the correlation between the two
constructs. According to Awang (2014; 2015; Hoque et al., 2017; Awang et al., 2015a; Kashif et
al., 2016), Discrimination Validity will be achieved if all the root values of convergence validity
(AVE) (Diagonal) are greater than other values of both rows and columns. Findings from Table 5
show Discrimination Validity for all constructions in the model achieved.

Analysis of the Impact between Building Motivation, Learning Styles and Learning Disciplines

Analysis by using SEM yields a standard regression value between the construct and the usual
regression value and both have their own utility. Figure 6 shows the standard regression weight
findings, whereas Figure 7 shows a typical regression value as a result of the SEM procedure.

Figure 5: SEM Insights Shows the Standard Regression Value between Construction

An important summary of the SEM findings in Figure 5 (standard regression):

1) The R2 value for the construction of the Learning Discipline is 0.38. This shows the two
constructors constructed in the model (see arrow), namely Motivation (MTV) and
Learning Style (LS) which accounted for 38% of the Learning Discipline (LD) among the
populations in the study.
2) The value of R2 to build AA_AM (Academic Achievement Additional Mathematics) is
0.75. This shows three constructs of predictors in the model (see arrow), namely
Motivation, Learning Styles and Learning Discipline contributing 75% to AA_AM among
the populations in the study.
3) The correlation value between two free constructs on the model shown by double-
headed arrows is as follows: The correlation between Motivation and Learning Styles
is 0.54. This shows that the SEM model is valid and has no multicollinearity problem.

Figure 6 shows the findings of regression values between the constructs in the model, to
build the required regression equation and to test the next hypothesis.

780
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

Figure 6: SEM Findings Indicate the Regression Value between Constructs

An important summary of the SEM findings in Figure 6 (regression value):

Regression equations for Learning Discipline (LD) and AA_AM are as follows

LD = 0.93MTV + 0.08LS (R2 = 0.38)

AA_AM = 0.37MTV + 0.19LS + 0.70LD (R2 = 0.75)

Furthermore, the researcher will test every hypothesis proposed in this research. Table 6
shows the approximation of the direct effects of the effects of each independent construct on
the dependent construct in the model as shown in Figure 6 above.

Table 6: Regression Coefficients between Construct Value and Probability (p)

Construct Construct Estimate S.E. C.R. P Label
AA_AM <--- Motivation 0.368 0.105 3.498 *** Significant
Learning <--- Motivation 0.933 0.145 6.426 *** Significant
Discipline
AA_AM <--- Learning 0.703 0.055 12.731 *** Significant
Discipline
AA_AM <--- Learning 0.188 0.081 2.323 0.020 Significant
Styles
Learning <--- Learning 0.083 0.125 0.664 0.507 Not Significant
Discipline Styles
*** Significant value at the level of significance, p<0.001

Table 7 shows the results of hypothesis testing of the direct effect of independent
construct on dependent construct. Hypothesis testing in Table 7 is based on the SEM findings
from Figure 6 above.

781
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

Table 7: Hypothesis Test of Direct Impact between Constructs

Direct Effect Hypothesis P Decision
H1 : Motivation has a significant direct impact on academic *** Supported
achievement
H2 : Motivation gives a significant direct impression on *** Supported
learning discipline
H3 : Learning discipline has a significant direct impact on *** Supported
academic achievement
H4 : Learning styles have a significant direct impact on 0.020 Supported
academic achievement
H5 : Learning styles have a significant direct impact on learning 0.507 Not supported
discipline

Impact of Motivation on Academic Achievement

Table 6 shows that motivation has a significant direct impact on academic achievement with
estimated regression value (β) is 0.368 at significant level 0.001 (Estimate=0.368, CR=3.498,
p<0.001). This means that the construction of Motivation has a positive and significant influence
on the construction of Academic Achievement. This means that if the Motivation increased by 1
unit, Academic Achievement will increase by 0.368 units. The findings of this study indicate that
the construct of Motivation has a positive and significant influence on the development of
Academic Achievement.

Impact of Motivation on Learning Discipline

Table 6 shows that motivation has a significant direct impact on the learning discipline with an
estimate of regression value (β) is 0.933 at a significant level of 0.001, (Estimate=0.933, CR=6.426,
p<0.001). This means that the construction of Motivation has a positive and significant influence
on the construction of the Learning Discipline. This means that when Motivation increases by 1
unit, the Learning Discipline will increase by 0.933 units. The findings of this study indicate that
the construct of Motivation has a positive and significant influence on the constructs of the
Learning Discipline.

Impact of Learning Discipline on Academic Achievement

Table 6 shows that the discipline of learning has a significant direct impact on academic
achievement with an estimate of regression value (β) is 0.703 at a significant level of 0.001
(Estimation=0.703, CR=12.731, p<0.001). This means that the construction of Discipline Learning
has a positive and significant influence on the construction of Academic Achievement. This means
that if the Learning Discipline increased by 1 unit, Academic Achievement will increase by 0.703
units. The findings of this study indicate that the construction of Discipline Learning has a positive
and significant influence on the construction of Academic Achievement.

782
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

Impact of Learning Style Effects on Academic Achievement

Table 6 shows that learning styles have a significant direct effect on academic achievement with
regression value estimation (β) is 0.188 at a significant level of 0.020 (Estimate=0.188, CR=2.323,
p<0.001). This means that the Learning Style construction has a positive and significant influence
on the construction of Academic Achievement. This means that if the Learning Styles increase 1
unit, Academic Achievement will increase by 0.188 units. The findings of this study indicate that
the construction of Learning Style has a positive and significant influence on the construction of
Academic Achievement.

Impact of Learning Styles on Learning Discipline

Table 6 shows that learning style has no significant effect on learning discipline with regression
value estimation (β) is 0.083 at significant level of 0.507 (Estimation=0.083, CR=0.664, p<0.001).
This means that the Learning Style constructs have no positive and not insignificant influence on
the construction of the Learning Discipline. The findings of this study indicate that the
construction of Learning Style has no positive and not insignificant effect on the construction of
Learning Discipline.

Intermediate Analysis (Mediator) for Development of Learning Discourse

Table 8 shows hypotheses testing the influence of mediators of the Learning Discipline construct
in the relationship between two free construction (Motivation and Learning Style) and
dependent construct Academic Achievement (AA_AM).

Table 8: Hypothesis Test of Mediator Effect Learning Discourse Structure

Testing Hypothesis for Mediator Decision
H6 : The discipline of learning is the mediator of the relationship Supported
between motivation and academic achievement See the test in Figure 7
H7 : The discipline of learning is the mediator of the relationship Not supported
between learning styles and academic achievement See the test in Figure 8

Discipline Learning Is a Mediator Relationship between Motivation and Academic Achievement

Figures 7 and Table 8 illustrate the mediator's testing procedure in the model by Awang (2012;
2014; 2015). In this model, Learning Discipline (LD) is an intermediate variable, Motivation (MTV)
is an independent variable and Academic Achievement (AA_AM) is a dependent variable.
Findings indicate that intermediate contact tests are supported and the type of intermediate
contact is Partial Mediation as a direct effect of Motivation (MTV) on the Learning Discipline (LD)
and Learning Discipline (LD) to significant Academic Achievement (AA_AM), and the direct effect
of Motivation (MTV) on Academic Achievement (AA_AM) is also significant. The bootstapping
findings also show full mediation because of a non-significant direct effect and are consistent
with the findings of interstitial exams in the testing procedure.

783
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

Figure 7: Hypothesis Testimonial Test of MTV-LD-AA_AM Mediator Procedure

Learning Discipline Is a Mediator Relationship between Learning Styles and Academic

Achievement

Figures 8 and Table 8 illustrate the procedure of mediator test in the model according to Awang
(2012; 2014; 2015). In this model, Learning Discipline (LD) is an intermediate variable, Learning
Style (LS) is an independent variable and Academic Achievement (AA_AM) is a dependent
variable. Findings indicate that intermediary linking tests are not supported and the type of
intermediate relationship cannot be applied, because the direct effect of Learning Styles (LS) on
the Learning Discipline (LD) is not significant. The bootstrapping findings also do not show any
mediation due to indirect messages indicating no significant inconsistencies with the results of
mediation in the test procedure.

Figure 8: Hypothesis Testimonial Test of LS-LD-AA_AM Mediator Procedure

784
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

Conclusion
Overall, the CFA analysis carried out on the measurement model for the construction of
motivation, learning styles and learning discipline, has been shown to have reached the fitness
index. While the combined factorization analysis of all measurement models (Pooled CFA) shows
that the three categories of model compatibility indexes for all models of construction constructs
have been achieved and discriminant validity for all constructions in the model has also been
achieved. Inference analysis findings also show motivation, learning styles and learning discipline,
have a positive and significant influence on academic achievement. Furthermore, motivation also
has a positive and significant impact on the learning discipline, but the learning style has no
positive and not significant effect on the learning discipline. Intermediate analysis findings for
the learning discipline take place between motivation and academic achievement and do not
occur between learning styles and academic achievement.

Acknowledgement
Special appreciation is owed to Universiti Sultan Zainal Abidin (UniSZA), Research Management,
Innovation & Commercialization Centre (RMIC) UniSZA & Ministry of Higher Education Malaysia
(MOHE).

Corresponding Author
Abdul Hakim Abdullah
Faculty of Islamic Contemporary Studies, Universiti Sultan Zainal Abidin, Gong Badak Campus,
21300 Kuala Terengganu, Terengganu, Malaysia. Email: hakimabd@unisza.edu.my

References
Alias, H & Hartini Husain (2017). Structural Equation Modelling (SEM) & manual AMOS graphic
(edisi Bahasa Melayu) Modul 1.

Awang, Z. (2011). A handbook on SEM: Structural Equation Modelling. Kelantan: Universiti

Teknologi MARA.

Awang, Z. (2012). Research methodology and data analysis. Penerbit Universiti Teknologi MARA
Press (UiTM Press).

Awang, Z. (2015). SEM Made Simple: A Gentle Approach to Learning Structural Equation
Modeling. Bandar Baru Bangi, MPWS Rich Resources.

Awang, Z., Afthanorhan, A., Mohamad, M., & Asri, M. A. M. (2015a). An evaluation of
measurement model for medical tourism research: the confirmatory factor analysis
approach. International Journal of Tourism Policy, 6(1), 29-45.

Yahaya, A. & Obih, D. M. (2010). Hubungan antara motivasi, gaya pembelajaran dan iklim bilik
darjah terhadap pencapaian akademik pelajar tingkatan empat. In: National Seminar
Sabah.

785
International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

Bambang Sumantri (2010). Pengaruh disiplin pembelajaran terhadap prestasi pembelajaran

siswa kelas XI SMK PGRI 4 Ngawi Tahun Pelajaran 2009/2010. Yayasan STKIP PGRI Ngawi,
Media Prestasi Vol.. VI No. 3 Edisi Desember 2010.

Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological bulletin, 107(2),
238.

Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation
models. Sociological Methods and Research, 21, 205–229.

Brown, M. W., and R. Cudeck. (1993). Alternative Ways of Assessing Model Fit. Sage focus
editions, 154, 136-136.

Byrne, B. M. (1998). Structural equation modeling with LISREL, PRELIS and SIMPLIS: Basic
concepts, applications and programming. Mahwah, New Jersey: Lawrence Erlbaum
Assiociates.

Byrne, B. M. (2001). Structural equation modeling with AMOS: Basic concepts, applications, and
programming. Mahwah, NJ: Lawrence Eribaum Associates.

Byrne, B. M. (2013). Structural equation modeling with AMOS: Basic concepts, applications, and
programming (2nd ed.). New York: Routledge.

Chua, Y. P. (2014d). Kaedah dan statistik penyelidikan: Ujian regresi, analisis faktor, dan analisis
SEM, Buku 5 (edisi 2). Selangor: McGraw-Hill Education (Malaysia) Sdn. Bhd.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivatiate data
analysis (6th ed.). New Jersey: Pearson Education International.

Hoque, A.S.M.M, Awang, Z., Jusoff, K., Salleh, F., and Muda, H (2017). Social Business Efficiency:
Instrument Development and Validation Procedure using Structural Equation Modelling.
International Business Management, 11(1), 222-231.

Joachim Joseph Masilamany (2014). Motivation and learning styles in relation to academic
achievement in English language. Master’s Project, Open University Malaysia (OUM).

Joreskog, K. G., & Sorbom, D. (1993). LISREL 8, Structural equation modelling with the SIMPLIS
command language. Chicago, IL: Scientific Software International Inc.

Kashif, M., Samsi, S. Z. M., Awang, Z., & Mohamad, M. (2016). EXQ: measurement of healthcare
experience quality in Malaysian settings: A contextualist perspective. International
Journal of Pharmaceutical and Healthcare Marketing, 10(1), 27-47.

786
 International Journal of Academic Research in Business and Social Sciences
Vol. 8 , No. 4, April 2018, E-ISSN: 2 22 2 -6990 © 2018 HRMARS

Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). New York:
The Guilford Press.

Locke, E. A. (2005). "Why emotional intelligence is an invalid concept". Journal of Organizational

Behavior. 26(4): 425.

Marsh, H. W., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of
selfconcept: First-and higher order factor models and their invariance across groups.
Psychological bulletin, 97(3), 562.

Nelson B. J. (2003). An investigation of the impact of learning style factors on, college Students
retention and achievement. Disertasi education doctor. St. John’s University. Dissertation
Abstracts International 53 (09): 3121.

Schumucker, R. E. & Lomax, R. (2004). A beginner’s guide to structural equation modeling (2nd
ed.). Mahwah, New Jersey: Lawrence Erlbaun Associates Publishers.

Selmes I. P. (1987). Improving study skills: Changing perspective in education. Great Britain:
Hodder and Stoughton Ltd.

Wanqing, C. & Abdullah, K. I. (2012). Motivation of hep and lep learners in the globalised world-
goals, attitudes and self-related beliefs. Masters thesis, Universiti Teknologi Malaysia.

787

View publication stats