INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XXVI October 2025 | Special Issue on Education
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Lecturer Teaching Style and Learning Style do not affect academic
Achievement: A Report on Evidence of a Mediating Variable
Kurnaemi Anita, Muhammad Nirwan Idris, Muhammad Isra Syarif
Institusi Agama Islam STIBA Makassar, Indonesia
DOI: https://dx.doi.org/10.47772/IJRISS.2025.903SEDU0691
Received: 10 November 2025; Accepted: 20 November 2025; Published: 25 November 2025
ABSTRACT
Purpose
The roles of lecturers' teaching styles and students' learning styles remain uncertain regarding their impact on
academic performance. This study investigates the effects of these styles on academic achievement among
religious students, aiming to develop new strategies for the teaching and learning processes.
Methodology
The participants in this research were religious students from South Sulawesi, Indonesia, who actively engaged
in their studies from 2021 to 2024, comprising a total of 754 individuals (43.5% male, 56.5% female). The
data is analyzed using SEM-PLS software.
Findings
The findings indicate that neither of these variables significantly influences academic achievement unless
mediated by intelligence. Intelligence acts as a mediating variable, facilitating the influence of lecturers'
teaching styles and students' learning styles on academic performance. The researchers argue that lecturers
play a crucial role in delivering instructional content through appropriate methods, designing engaging
learning activities, and providing memorable educational experiences for students.
Novelty
This study is the first to compile 7 variables that are presumed to influence academic achievement, using 2
mediating variables.
Significance
This study suggests that innovations in teaching practices and enhanced student interactions are crucial for
fostering intelligence. Such developments enable students to absorb knowledge effectively, refine their
abilities, and cultivate the learning experiences acquired in class, which are essential for navigating future
professional environments.
Keywords: Mediating variable, Structural Equation Modeling, Influence, Intelligence, learning styles, teaching
styles.
INTRODUCTION
Academic achievement is regarded as a key indicator of student success within higher education (Kell et al.,
2013; Steinmayr et al., 2018). It influences future life outcomes (Hanushek, 2020) and reflects students'
productivity and intellectual capabilities (Sothan, 2019). The relationship between students and lecturers is
considered one of the critical determinants of learning achievement, established through fostering a positive
rapport via character development and social understanding of ethics, manners, and norms (Berhanu &
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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Sabanci, 2020). Collaboration among schools, families, and diverse teaching styles forms a comprehensive
system aimed at achieving learning objectives by accommodating the varying characteristics of students
(Khumalo & Utete, 2023; Sadiku & Sylaj, 2019). Theoretically, academic achievement can be supported by
external factors, such as learning facilities, lecturers' teaching methods, parental involvement, and the
surrounding environment (Chan & Dai, 2023), while internal factors stem from the students themselves
(Ozcan, 2021). Consequently, educators are required to think creatively in employing a variety of teaching
styles tailored to the specific needs of their students (Tang et al., 2022), ensuring a more effective educational
experience.
Academic quality is reflected in students' achievements, serving as a measurable indicator (Oyewobi et al.,
2020). These achievements are significantly influenced by ongoing efforts to enhance educational standards
and academic research (Ede & Igbokwe, 2018). Key variables closely associated with academic success
include intelligence, motivation, and academic interest (Arthur & Everaert, 2012; Duff & Mladenovic, 2015;
Sternberg, 2019). Additionally, diverse learning styles among students can impact their performance (Dryer et
al., 2016). Each student has a preferred learning method, which helps alleviate the burden of coursework
(Kumar et al., 2017). This study will empirically investigate seven variables, demonstrating that the interaction
between lecturers' teaching styles and students' learning preferences does not directly influence academic
achievement without the presence of an intervening variable. Ultimately, understanding these dynamics is
essential for improving educational outcomes and fostering effective teaching and learning environments.
LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT
Numerous studies suggest that student understanding is significantly influenced by lecturers' teaching styles
(Esmail Sabra et al., 2018). Other researchers have identified a moderate correlation between teaching styles
and academic achievement (Chetty et al., 2019). It is recommended that lecturers align their practices with
effective teaching indicators and demonstrate mastery of the subject matter, as this significantly enhances
student comprehension (Husin et al., 2023; Keerthigha & Singh, 2023; Shaari et al., 2014). A lack of subject
mastery and ineffective presentation skills adversely impacts teaching styles (Esmail Sabra et al., 2018), which
consequently affects student interest and understanding (Chetty et al., 2019). Monotonous, one-dimensional
lecture formats often lead to disengagement and reduced focus among students (Tang et al., 2022). While some
studies have found no significant correlation between teaching style and student academic achievement (Shaari
et al., 2014), it remains vital to explore these dynamics further. In this study, the variable of lecturers' teaching
style will be measured using four indicators on a Likert scale of 1-6 (ranging from strongly agree to strongly
disagree), hypothesising the following:
H1: Lecturer teaching style does not significantly affect academic achievement.
Learning style variables have been investigated by Vidyakala et al. (2019), who studied a sample of 103
students in India, and by Thu Ha (2021), who examined 307 students in Thai Nguyen City, Vietnam. Both
studies found a significant correlation between student learning styles and academic achievement. However,
some researchers did not identify a significant relationship between these variables(Awang et al., 2017; Kohan
et al., 2021; Mozaffari et al., 2020). They argue that each student possesses a distinct learning style, which may
not be the primary determinant of academic success. Nevertheless, learning styles and patterns significantly aid
students in acquiring information and knowledge (Ahinful et al., 2019). A learning style refers to an
individual's preferred approach to absorbing information in a way that is most comfortable and memorable
(Mozaffari et al., 2020; Vidyakala et al., 2019), In this study, the learning style variable is measured using four
indicators on a Likert scale of 1-6 (from strongly agree to strongly disagree). The hypothesis posits:
H2: Learning style does not affect academic achievement.
Intelligence as a cognitive element plays a quite influential role, and is often considered the main factor in
determining a person's success (Bate et al., 2022; Iqbal et al., 2021). Intelligence positively and significantly
correlates with student academic achievement (Lozano-Blasco et al., 2022; Quilez-Robres et al., 2021; Zhoc et
al., 2018). 212 correspondents were asked to complete the Raven's Progressive Matrix, Bar-On Emotional
Intelligenc e Inventory, and Emo Sensory Intelligence Scale and then matched with GPA as a measure of their
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
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academic achievement . The results showed that students' IQ and EQ levels could significantly predict
academic achievement and proved to be positive predictors of academic success (Pishghadam et al., 2022).
Other researchers found no relationship between intelligence and academic achievement in both male and
female students (Iqbal et al., 2021). We will connect the previous two variables with intelligence as an
intervening factor with the following hypothesis design:
H3: Intelligence significantly directly affects academic achievement.
Other factors that support academic achievement are physiology, academic interest, motivation, and learning
environment. We will also investigate these variables to compare with lecturer's teaching style and learning
style. There is a significant correlation between fitness and academic achievement (Donnelly, 2017; Niet et al.,
2014; Redondo-Flórez et al., 2022; Shook, 2016). Other researchers claim that there is no significant effect
between physical fitness and academic achievement (Bilgin et al., 2020). Physiological includes the physical
condition of the body in supporting learning activities such as adequate nutrition, a body that feels fresh and
healthy, and is not easily tired (Donnelly, 2017; Niet et al., 2014), and optimal sensory function conditions
(Shook, 2016).
Interest is a source of motivation that encourages people to do what they want (Longobardi et al., 2018;
Mappadang et al., 2022), including students' perceptions of the lesson, students' physical and psychological
conditions, the attractiveness of the subject matter to students' lives, teachers' teaching methods and styles, and
motivation (Blankenburg et al., 2016). Some researchers found a correlation between intrinsic interest and
academic achievement (Ahinful et al., 2019; Blankenburg et al., 2016; Duff & Mladenovic, 2015; Fallan &
Opstad, 2014; Lee et al., 2014; Mappadang et al., 2022). But Meyer et al (2019) argue that it is academic so
that interest does not correlate with academic achievement.
Several researchers proved the correlation between motivation and academic achievement (Foong & Liew,
2022; Goodman et al., 2011; Sivrikaya, 2019; Yarin et al., 2022). However, Bakar et al (2022) did not find the
effect of learning motivation on academic achievement. Motivation serves as a driver for action and leads to
the goal to be achieved (Yarin et al., 2022), which is determining what activities must be done to achieve
targets and goals by setting aside activities that are not beneficial to the goal (Bin Abdulrahman et al., 2023;
Sharma & Sharma, 2018; Sivrikaya, 2019). Supported by the learning environment is a place where students
can interact with their environment (Al-Qahtani, 2015; Edgerton et al., 2011; Mørk et al., 2020), so that
students can learn conducively to achieve academic achievement. Some of these variables can be hypothesized
as follows:
H4: Physiological significantly directly affects academic achievement.
H5: Academic interest significantly directly affects
academic
achievement.
H6: Motivation significantly directly affects academic achievement.
H7: Learning environment significantly directly affects academic achievement.
METHODOLOGY
Participant
The research contributors were students in the field of religion who were still actively studying in 2020-2023
from 3 universities in South Sulawesi Indonesia, totaling 5674 populations. Hair et al (2021) state that the
sample size used for PLS-SEM research can use the method recommended by Kock & Hadaya (2016), namely
using the exponential gamma distribution (for the lower limit) and the inverse square root (for the upper limit).
The larger the sample in PLS, the more it can increase the precision or consistency of the estimated PLS
parameters (Hair et al., 2021).
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The sample size of this study is based on the strength of statistical tests in general of 80%, the significance of
the path coefficient or the assumption of influence between variables lies in the interval 0.11-0.2, and the alpha
level of significance of 5%. Based on the sample size obtained from the above formula, the research
respondents must be at least 155. A total of 800 questionnaires were distributed and 754 correspondents met
the requirements of the analysis test. Academic achievement as the dependent variable is measured by the
results of Grade Point Average (GPA) (Abbassi et al., 2018; Ezenwoke et al., 2020; Pérez-López & Ibarrondo-
Dávila, 2019). GPA ratio data were transformed and categorized: if GPA ≥ 3.76 = 3 (High), GPA 3.25-3.75 =
2 (Average) and if GPA < 3.25 = 1 (Low).
Table 1. Variable Description
Characteristics
Categories
Indicator
Number
Percentage
Variable
Phys
3
IQ
4
AI
6
LE
4
MOT
5
LTS
4
LS
3
GPA
≥ 3.76 = 3 (High)
400
53.05%
3.25-3.75 = 2 (Medium)
344
45.62%
< 3.25 = 1 (Low)
10
1.33%
Gender
Male
328
43.50%
Female
426
56.50%
Note: Academic achievement (AA) are measured based on Grade Point Average (GPA). Physiological (Phys),
Intelligence (IQ), Academic interest (AI), Learning environment (LE), Motivation (MOT), Lecturer teaching
style (LTS), Learning style (LS).
Data Analysis
Data analysis used PLS-SEM, a multivariate statistical approach that allows for the simultaneous estimation of
multiple relationships between variables, commonly applied in prediction, exploration, and structural model
development research (Hair et al., 2019) In mediation analysis, we examine whether changes in independent
constructs result in changes in mediator variables that will affect the dependent construct in a model. The
purpose of the measurement model is to evaluate the extent to which manifest variables effectively represent
each exogenous and endogenous latent variable (Dawson, 2014; Demming et al., 2017; Hair et al., 2017;
Henseler et al., 2014; Sarstedt et al., 2017). Research involving causal chains, such as "A influences B, which
in turn influences C," or more complex nomological networks with intervening variables (mediation), is not
suitable for using simple linear regression methods because the measurement model estimates are done
partially and not simultaneously, with consequences for the quality of the results that are not accurate (Hair et
al., 2021; Henseler et al., 2015; Sarstedt et al., 2020). PLS-SEM applies two evaluation models, the structural
model (inner model) as a representative of the structural paths in the form of constructs (Henseler et al., 2015;
Risher & Hair, 2017), and the measurement model (outer model), which describes the connection between
each construct and its associated indicators (Dijkstra & Henseler, 2015; Hair et al., 2021).
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RESULTS
Proof of Hypothesis
Table 1 reveals that the majority of respondents are female (56.5%), with males constituting 43.5%. The
respondents' Grade Point Average (GPA) was generally dominated by a high GPA (53.05%), then medium
(45.62%), and low (1.33%). This shows that academic achievement indicators can generally be measured well.
Table 2 outlines the statistical properties of the research variables, including the mean, median, standard
deviation, minimum and maximum values, kurtosis, and skewness. It also shows that all data items are
normally distributed except item (AI 2) with an excess kurtosis value above 2 (2.496). The distribution of data
for the academic interest variable indicates a minimum score of 1, a maximum score of 6, an average mean
value (M = 4.8), and an average standard deviation (SD = 0.88). These results indicate that students' academic
interest is relatively large by looking at the proximity of the mean value and the maximum scale of 6 or it can
be interpreted that interest and making their campus their first choice and priority is in the high category,
which is 80%. Likewise, the variance of the data is relatively large by looking at the distance of the mean value
and standard deviation.
The lecturer teaching style variable measured by 4 indicators in the form of lecturer personality, teaching
methods, achievements, and involving students shows a minimum value of 1 and a maximum of 6, with a
mean value (M = 4.63), and standard deviation (SD = 0.82). This suggests that, on the whole, lecturers
demonstrate a relatively strong performance in terms of their personality, achievements, student engagement,
and teaching methods, with 77% of lecturers falling into the "fairly good" category. The learning style
variable, measured by three indicators: visual, auditory, and kinesthetic, shows a minimum value of 1 and a
maximum of 6, with a mean value (M = 4.2), and standard deviation (SD = 0.62). These results indicate that
students' learning styles are generally a mix of visual and audio learning styles by 70%.
Table 2 Descriptive Statistics
Name
Mean
Median
Scale
min
Scale
max
Std.
dev
Excess
kurtosis
Skewness
Cramér-von Mises p
value
AI.1
4.429
4.333
1
6
1.014
-0.561
-0.141
0
AI.2
5.017
5
1
6
0.771
2.496
-0.860
0
AI.3
4.674
5
1
6
0.899
-0.302
-0.101
0
AI.4
4.859
5
1
6
0.894
-0.144
-0.368
0
AI.5
5.202
5
2
6
0.832
-0.273
-0.698
0
AI.6
4.402
4.5
2
6
0.853
-0.045
-0.071
0
LE.1
3.908
4
1
6
0.867
0.101
-0.01
0
LE.2
3.908
4
1
6
0.966
0.282
0.035
0
LE.3
4.358
4
1
6
1.036
0.477
-0.256
0
LE.4
4.21
4
1
6
1.016
0.038
-0.155
0
Phys.1
4.25
4.25
1
6
0.95
-0.287
-0.219
0
Phys.2
3.521
3.333
1
6
0.851
0.058
0.311
0
Phys.3
4.096
4
1
6
0.77
0.516
-0.006
0
LTS.1
4.455
4.5
1
6
0.795
0.353
-0.159
0
LTS.2
4.852
5
1
6
0.79
-0.095
-0.32
0
LTS.3
4.438
4.5
1
6
0.854
0.729
-0.277
0
LTS.4
4.76
5
1
6
0.845
0.113
-0.328
0
MOT.1
3.42
3.5
1
6
1.368
-0.635
0.003
0
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MOT.2
4.904
5
1
6
0.78
0.691
-0.472
0
MOT.3
4.302
4.333
1
6
0.963
0.111
-0.472
0
MOT.4
4.684
4.75
2
6
0.74
0.187
-0.308
0
MOT.5
4.94
5
2
6
0.869
0.28
-0.728
0
IQ.1
4.054
4
1
6
0.8
0.139
-0.014
0
IQ.2
3.794
3.75
1
6
0.74
0.352
0.053
0
IQ.3
3.883
4
1
6
0.751
-0.062
0.111
0
IQ.4
4.025
4
1
6
0.69
0.315
0.223
0
LS.1
4.242
4.3
1
6
0.587
1.65
-0.303
0
LS.2
4.347
4.3
1
6
0.656
0.137
-0.023
0
LS.3
3.97
4
1
6
0.615
0.788
-0.152
0
AA
3.737
3.77
3.06
4
0.181
0.812
-0.891
0
The learning environment measured by 4 indicators shows a minimum value of 1 and a maximum of 6, the
average mean value (M = 4.096), and the average standard deviation (SD = 0.97). This indicates that the
atmosphere students feel in the learning environment is quite good, which is 68%, with a relatively large data
variance by looking at the close mean value and standard deviation. 3 indicators measure physiological
variables show a minimum value of 1 and a maximum of 6, the average mean value (M = 3.96) and the
average standard deviation (SD = 0.86), in general students have a good physique and maintain diet and
hygiene with a score of 66%.
The results of motivation variable show that this variable has an average mean value (M = 4.45) and an
average standard deviation (SD = 0.94). It can be interpreted that student motivation internally (factors from
within) and externally (factors from outside) is in the good category of 74%. The average mean value of
intelligence variable (M = 3.94) and the average standard deviation (SD = 0.75) show that the level of
intelligence of students is in the medium or high enough category by 66%.
Evaluation of the Reflective Measurement Model
Based on Figure 1, all variables are measured by valid indicators with outer loading between 0.804 - 0.890,
indicating that the indicators are valid to reflect the measurement of academic interest, learning environment,
physiological, lecturer teaching style, motivation, intelligence, and learning style. This finding concludes that
the influence of all exogenous variables (learning environment, physiological, lecturer teaching style, learning
style, academic interest) on endogenous variables (intelligence, motivation and academic achievement), as well
as between endogenous variables is linear or the linearity effect of the model is fulfilled (robust).
Figure 1 Output of Loading Factor
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Table 3 Measurement Model Test Output
Variables
Cronbach’s Alpha
Composite Reliability
AVE
Academic Interests
0.898
0.924
0.710
Learning environment
0.895
0.925
0.754
Physiological
0.771
0.866
0.684
Lecturer teaching style
0.910
0.937
0.788
Motivation
0.807
0.886
0.721
Intelligence
0.739
0.852
0.657
Learning style
0.776
0.868
0.687
The reliability of the variables is deemed satisfactory. As presented in Table 3 both cronbach’s alpha and
composite reliability exceed 0.70, indicating that the measurement items are both consistent and dependable in
assessing the variables. Additionally, the convergent validity, with an Average Variance Extracted (AVE)
greater than 0.50, satisfies the criteria for good convergent validity. In general, the variance explained by the
measurement items within the variables ranges from 65.7% to 78.8%.
Table 4 Output of Fornell-Larcker and Heterotrait-Monotrait Ratio (HTMT)
Fornell-Larcker
Heterotrait-Monotrait Ratio (HTMT)
Phys
LS
LTS
IQ
LE
MA
MO
T
A
A
Phy
s
LS
LS
T
IQ
LE
AI
MO
T
A
A
Phy
s
0.82
7
LS
0.45
3
0.82
9
0.58
0
LT
S
0.47
5
0.43
9
0.88
8
0.56
1
0.52
3
IQ
0.37
5
0.46
3
0.35
2
0.81
1
0.50
1
0.59
0
0.42
7
LE
0.35
5
0.34
5
0.49
6
0.21
3
0.86
9
0.43
0
0.41
6
0.55
5
0.24
8
AI
0.46
7
0.47
5
0.55
4
0.34
0.42
7
0.84
3
0.55
9
0.56
1
0.61
4
0.42
0
0.47
9
MO
T
0.47
0.61
5
0.54
3
0.41
5
0.42
7
0.71
0.84
9
0.60
5
0.76
1
0.62
4
0.53
2
0.50
7
0.82
1
AA
-
0.15
1
-
0.07
9
-
0.08
5
0.04
-
0.09
-
0.00
1
-
0.01
7
1
0.16
8
0.09
3
0.08
8
0.04
7
0.08
9
0.02
1
0.02
9
The physiological variable has a root AVE (0.827) greater correlation than other latent variables in Table 4.
These results indicate that the discriminant validity of the academic interest variable is met. In the sense that
the measurement items of physiological variables focus on measuring physiological variables and are low in
measuring other variables, or physiological variables divide their variance more to their measurement items
than to other variable measurement items. Likewise, the learning style variable has an AVE root (0.829),
lecturer teaching style (0.888), intelligence (0.811), learning environment (0.869), academic interest (0.843),
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and motivation (0.849), the overall AVE root of these variables is greater than the correlation of other latent
variables. It is concluded that overall these measurement variables have good discriminant validity according
to the Fornell-Larcker method. The test results in Table 4 show that the HTMT value of each variable that
correlates with other variables is below 0.90, so discriminant validity is said to be met.
Evaluation of Goodness and Fit of the Model
The qualitative interpretation value of R-square is 0.19 (low influence), 0.33 (moderate influence), 0.66 (high
influence) (Hair et al., 2021). The results of data processing explain the magnitude of the influence of lecturer
teaching styles and student learning styles on student intelligence by 23.8% (classified as an influence towards
the medium). The magnitude of the influence of academic interest on student motivation is 50.3% (classified
as an influence towards high). While the magnitude of the joint influence between lecturers' teaching styles,
student learning styles, academic interest, and motivation on academic achievement amounted to 3.1%
(classified as a very low influence). Q-square describes a measure of prediction accuracy, namely how well
each change in exogenous/endogenous variables is able to predict endogenous variables. This measure is a
form of validation in PLS-SEM to state the suitability of model predictions (predictive relevance). Based on
Table 5, the q-square value of all variables other than intelligence is in the high category (Q
2
≥ 0.35).
Table 5 Output of R-Square, Effect Size of Q-Square, and Standardized Root Mean Square Residuals
Variable and
Categories
R-
square
Adjusted
R-square
SSO
SSE
Q² (=1-
SSE/SSO)
SRMR
d_ULS
d_G
Chi-
square
NFI
IQ
0.242
0.238
1131
776.972
0.313
MOT
0.504
0.503
1131
643.709
0.431
PA
0.049
0.031
377
0
1
FIS
1131
716.281
0.367
GB
1131
714.71
0.368
GMD
1508
557.793
0.63
LA
1508
642.373
0.574
MA
1885
831.064
0.559
Saturated model
0.061
1.300
0.519
1179.24
0.800
Estimated model
0.080
2.247
0.585
1272.19
0.784
The SRMR value below 0.08 indicates a model fit. While the Goodness of Fit Index (GoF Index) is an overall
evaluation of the model which is an evaluation of the measurement model and the structural model. This GoF
Index can only be seen from the reflective measurement model, namely the root of the geometric
multiplication of the average communality with the average R-square. The interpretation of the GoF Index
value is 0.1 (low GoF), 0.25 (medium GoF) and 0.36 (high GoF) (Henseler et al., 2014; Wetzels et al., 2009).
GoF = √( Average of Average of – )
= √(0,714 0,257) = 0,429
The calculation results show that the GoF model is 0.429, including the GoF Index in the high category.
Empirical data can explain the measurement and structural models with a high level of fit.
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Table 6 PLS Predict Test
Q²predict
PLS-SEM_RMSE
PLS-SEM_MAE
LM_RMSE
LM_MAE
IQ.1
0.170
0.731
0.582
0.742
0.587
IQ.2
0.146
0.686
0.547
0.693
0.542
IQ.3
0.130
0.703
0.566
0.703
0.564
MOT.2
0.473
0.567
0.454
0.538
0.426
MOT.3
0.268
0.826
0.633
0.768
0.583
MOT.4
0.322
0.611
0.481
0.563
0.435
GPK
0.005
0.181
0.144
0.186
0.148
Based on Table 6, the results of data processing from 14 observations of the RMSE and MAE values, there are
8 PLS model measurement items with RMSE and MAE values lower than the LM (linear regression) model,
indicating that the proposed PLS model has medium predictive power and is considered appropriate.
Structural Model Evaluation (Hypothesis Test Interpretation)
Table 7 shows that the Inner VIF values of exogenous latent variables (physiological, lecturer teaching style,
learning style, learning environment, academic interest, and motivation) and endogenous variables (including
intelligence and academic achievement) are not multicollinear (inner VIF < 5). This finding strengthens the
parameter estimation results in PLS-SEM is robust (unbiased).
Table 7 Multicollinearity Test Output (Collinearity Statistics VIF)
Phys
LS
LTS
IQ
LE
AI
MOT
AA
Physiological
1.534
LS
1.238
1.831
LTS
1.238
1.818
IQ
1.37
LE
1.425
AI
1
2.26
MOT
2.649
AA
Table 8 Structural Model for Direct Effect and Mediation Test Output (Indirect Effect)
Hypothesis
Path
Coefficient
P-values
95% Confidence Interval
Path Coefficient
f-Square
Upsilon
V
Lower limit
Upper limit
H1. LTS --> AA
-0.062
0.19
-0.174
0.058
0.002
H1
a
. LTS --> IQ
0.184
0
0.102
0.269
0.036
H2. LS --> AA
-0.091
0.062
-0.187
0.007
0.005
H2a. LS --> IQ
0.382
0
0.292
0.47
0.155
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H3. IQ --> AA
0.125
0.02
0.028
0.228
0.012
H4. Phys --> AA
0.181
0.003
0.086
0.292
0.023
H5. AI --> AA
0.103
0.11
-0.033
0.244
0.005
H5. AI --> MOT
0.71
0
0.659
0.76
1.017
H6. MOT --> AA
0.057
0.249
-0.081
0.194
0.001
H7. LE --> AA
-0.058
0.182
-0.163
0.038
0.003
MA --> MOT --> PA
0.04
0.251
-0.057
0.14
0.002
GB --> IQ --> PA
0.048
0.029
0.01
0.094
0.02
GMD --> IQ --> PA
0.023
0.046
0.04
0.49
0.01
Intelligence acts as a mediator of the indirect effect of student learning styles on academic achievement, as
shown in Table 8, with a path coefficient of 0.048 and a p-value of 0.029 (p < 0.05). Likewise, it is proven that
intelligence acts as a mediator in mediating the indirect effect of lecturer teaching style on academic
achievement with a path coefficient (0.023) and p-value (0.046 < 0.05). However, motivation is not
empirically proven to mediate the effect of academic interest on academic achievement even though the path
coefficient is positive (0.040), because the p-value is greater than 5% (0.251 > 0.05).
DISCUSSION
In the first hypothesis (H1) there is no significant direct effect of lecturer teaching style on academic
achievement. This finding contradicts the findings of most researchers (Chetty et al., 2019; Esmail Sabra et al.,
2018; Husin et al., 2023; Keerthigha & Singh, 2023). If you look at the measurement items on the lecturer's
teaching style variable in Figure 1, the highest outer loading that reflects this variable is LTS 2 and LTS 3 in
the form of competencies possessed by lecturers and the way lecturers teach in class. Nevertheless, the results
of data processing found a simultaneous effect of lecturers' teaching styles on academic achievement through
the intervening variable of student intelligence. This is supported by the significant effect of lecturer teaching
style on student intelligence (H1
a
), and there is a significant effect of intelligence on academic achievement
(H3). The better the lecturer's teaching style, the more it supports student intelligence, and the higher the
student's intelligence, the greater the potential to improve academic performance. This shows that although the
lecturer's teaching style does not directly affect academic achievement, student intelligence mediates the effect
of the lecturer's teaching style on academic achievement. In line with the findings of Shaari et al (2014) who
also did not find a direct effect of lecturer teaching style on academic achievement, but found an indirect effect
of lecturer teaching style on academic achievement through moderating variables.
This finding shows that intelligence can support the influence of the lecturer's teaching style on improving
academic achievement. Students who have high intelligence can digest well any output given by lecturers with
any teaching methods and styles. This study recommends for lecturers to improve the quality of learning
through effective and quality teaching styles by combining various methods. Several kinds of teaching styles
can be applied by lecturers such as personal modeling, delegation, discussion, pedagogy, and lecture methods.
All of these methods can be combined according to the characteristics of the students and the course being
taught. The teaching style of lecturers also includes how lecturers can provide attention and warmth to students
so that a psychological relationship is built between them (Keerthigha & Singh, 2023).
Similarly, in the second hypothesis (H2) there is no significant direct effect of student learning styles on
academic achievement. This finding supports the research of (Awang et al., 2017; Kohan et al., 2021;
Mozaffari et al., 2020). Although there is no effect, this finding can still contribute to providing information
about the characteristics of student learning styles. The learning styles preferred by most students are visual
and kinesthetic. Nevertheless, there is a simultaneous influence of learning styles on academic achievement
through the mediating variable of intelligence. In other words, intelligence mediates the effect of learning style
on academic achievement. This is reinforced by the significant direct effect of learning style on intelligence
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Page 9128
(H2
a
). The better and better the quality of students' learning styles, the better their intelligence, due to the habit
patterns that train their brains and reasoning to continue to work and think positively. Simultaneously, students
who have high intelligence will be able to adjust to any learning style that has a positive impact on improving
their academic performance. Based on the significance of the path coefficient, we have constructed an
illustration depicting the role of mediating variables in the effect of lecturer teaching style and student learning
style on academic achievement, as shown in Figure 2.
Figure 2. Full Mediation or Partial Mediation Model Diagram
The statistical interpretation of the mediation effect is represented by Upsilon (V) values: 0.175 (high
mediation effect), 0.075 (medium mediation effect), and 0.01 (low mediation effect) (Lachowicz et al., 2018;
Ogbeibu et al., 2021). According to the calculations, the role of intelligence as a mediator in the indirect effect
of lecturer teaching style and student learning style on academic achievement at the structural level is
relatively low. However, within a 95% confidence interval, by conducting programs to improve intelligence,
this mediating role will increase by 9.4% in mediating student learning styles and increase by 49% in
mediating lecturer teaching styles to improve academic achievement.
Table 8 shows that intelligence has the highest direct effect on academic performance compared to learning
environment, physiology, lecturer teaching style, student learning style, academic interest, and motivation.
This finding indicates that any modification in intelligence is likely to result in a more pronounced
improvement in academic achievement. Consequently, it can be inferred that, for educational institutions
aiming to enhance academic performance through the influencing variables, prioritising the development of
intelligence is essential due to its dominant effect on academic outcomes. Based on the recommendations of
this study, one potential approach to enhancing student intelligence quality involves improving both the
teaching styles of lecturers and the learning styles of students, as these two variables significantly influence
intelligence.
CONCLUSION
This study contrasts with some previous research that suggests a significant influence of lecturer teaching style
and student learning style on academic achievement. However, this research may be unique in its approach, as
it positions intelligence as a mediating factor between these two variables and academic performance. In this
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ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XXVI October 2025 | Special Issue on Education
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context, intelligence is found to serve as an intermediary variable, effectively mediating the simultaneous
impact of both student learning style and lecturer teaching style on academic achievement. This novel
perspective contributes to the understanding of how cognitive abilities can influence the relationship between
teaching and learning processes and their outcomes in academic settings. Therefore, this study recommends
students to immediately recognize the characteristics and explore the potential of their respective learning
styles in order to get the appropriate formula to support the improvement of their intelligence so as to improve
academic achievement. This finding also recommends lecturers to be able to recognize and accommodate the
needs of students related to their learning styles to increase intelligence and academic achievement.
LIMITATIONS
While the findings have important implications, this research has several limitations. We only took data from 5
well-known universities in Eastern Indonesia, South Sulawesi. besides this research only focuses on observing
religious students. Actually, broader research has been studied by several researchers (Baharudin et al., 2017;
Jepsen et al., 2015; Mei Ph’Ng, 2018; Ph’ng et al., 2016). So that the results we get are different from similar
studies. The number of variables we took may also make the focus of the data on the lecturer's teaching style
and learning style biased so that the inconsistency of the expected results is obtained. We recommend a review
that looks at universities in an unrestricted way and focuses on these variables.
REFERENCES
1. Abbassi, F. A., Hussain, K., Farooq, S., & Akhtar, N. (2018). Best model selection for determinants of
students’ academic performance at tertiary level in azad Jammu and Kashmir, Pakistan. New
Educational Review, 51(1), 66–77. https://doi.org/10.15804/tner.2018.51.1.05
2. Ahinful, G. S., Tauringana, V., Bansah, E. A., & Essuman, D. (2019). Determinants of academic
performance of accounting students in Ghanaian secondary and tertiary education institutions.
Accounting Education, 28(6), 553–581. https://doi.org/10.1080/09639284.2019.1679204
3. Al-Qahtani, M. F. (2015). Associations between approaches to study, the learning environment, and
academic achievement. Journal of Taibah University Medical Sciences, 10(1), 56–65.
https://doi.org/10.1016/j.jtumed.2015.01.014
4. Arthur, N., & Everaert, P. (2012). Gender and Performance in Accounting Examinations: Exploring the
Impact of Examination Format. Accounting Education, 21(5), 471–487.
https://doi.org/10.1080/09639284.2011.650447
5. Awang, H., Abd Samad, N., Mohd Faiz, N. S., Roddin, R., & Kankia, J. D. (2017). Relationship
between the Learning Styles Preferences and Academic Achievement. IOP Conference Series: Materials
Science and Engineering, 226(1), 1–6. https://doi.org/10.1088/1757-899X/226/1/012193
6. Baharudin, A. F., Sahabudin, N. A., & Kamaludin, A. (2017). Behavioral tracking in E-learning by
using learning styles approach. Indonesian Journal of Electrical Engineering and Computer Science,
8(1), 17–26. https://doi.org/10.11591/ijeecs.v8.i1.pp17-26
7. Bakar, N. A., Alsmadi, M. S., Ali, Z., Shuaibu, A., & Solahudin, M. H. (2022). Influence of Students’
Motivation Influence of Students’ Motivation on Academic Achievement Among Undergraduate
Students in Malaysia. Journal of Positive School Psychology, 6(2), 3443–3450.
8. Bate, G. P., Velos, S. P., Gimena, G. B., & Go, M. B. (2022). Influence of IQ and Personality on
College Students’ Academic Performance In A Philippine State University. Journal of Positive School
Psychology, 2022(4), 5876–5882. http://journalppw.com
9. Berhanu, K. Z., & Sabanci, A. (2020). Factors Influencing University Students’ Academic Achievement
and Strategies Taken to Improve Their Achievement: Ethiopia as a Sample. İnönü Üniversitesi Eğitim
Fakültesi Dergisi, 21(3), 1165–1180. https://doi.org/10.17679/inuefd.559972
10. Bilgin, E., Bulca, Y., & Demirhan, G. (2020). Relationships Between Physical Activity Level, Health-
Related Fitness, Academic Achievement, and Academic Self-Concept. Education and Science, 45(202),
1–15. https://doi.org/10.15390/EB.2020.8343
11. Bin Abdulrahman, K. A., Alshehri, A. S., Alkhalifah, K. M., Alasiri, A., Aldayel, M. S., Alahmari, F. S.,
Alothman, A. M., & Alfadhel, M. A. (2023). The Relationship Between Motivation and Academic
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XXVI October 2025 | Special Issue on Education
www.rsisinternational.org
Page 9130
Performance Among Medical Students in Riyadh. Cureus, 15(10), 1–9.
https://doi.org/10.7759/cureus.46815
12. Blankenburg, J. S., Höffler, T. N., Peters, H., & Parchmann, I. (2016). The effectiveness of a project day
to introduce sixth grade students to science competitions. Research in Science and Technological
Education, 34(3), 342–358. https://doi.org/10.1080/02635143.2016.1222361
13. Chan, T. J., & Dai, M. (2023). Factors Influencing Academic Achievement of University Students.
LANGUAGE AND CULTURE Journal of Communication, Language and Culture, 3(2), 14–26.
https://doi.org/10.33093/jclc.2023
14. Chetty, N. D. S., Handayani, L., Sahabudin, N. A., Ali, Z., Hamzah, N., Rahman, N. S. A., & Kasim, S.
(2019). Learning styles and teaching styles determine students’ academic performances. International
Journal of Evaluation and Research in Education (IJERE), 8(3), 610–615.
https://doi.org/10.11591/ijere.v8i3
15. Dawson, J. F. (2014). Moderation in Management Research: What, Why, When, and How. Journal of
Business and Psychology, 29(1), 1–19. https://doi.org/10.1007/s10869-013-9308-7
16. Demming, C. L., Jahn, S., & Boztug, Y. (2017). Conducting Mediation Analysis in Marketing Research.
Marketing ZFP, 39(3), 76–98. https://doi.org/10.15358/0344-1369-2017-3-76
17. Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS Quarterly:
Management Information Systems, 39(2), 297–316. https://doi.org/10.25300/MISQ/2015/39.2.02
18. Donnelly, J. A. (2017). The Relationship Between Physical Fitness and School Performance. Journal of
Social, Behavioral, and Health Sciences, 11(1), 231–244. https://doi.org/10.5590/jsbhs.2017.11.1.16
19. Dryer, R., Henning, M. A., Tyson, G. A., & Shaw, R. (2016). Academic Achievement Performance of
University Students with Disability: Exploring the Influence of Non-academic Factors. International
Journal of Disability, Development and Education, 63(4), 419–430.
https://doi.org/10.1080/1034912X.2015.1130217
20. Duff, A., & Mladenovic, R. (2015). Antecedents and consequences of accounting students’ approaches
to learning: A cluster analytic approach. British Accounting Review, 47(3), 321–338.
https://doi.org/10.1016/j.bar.2014.06.003
21. Ede, M. O., & Igbokwe, U. O. (2018). Meta-analysis of the effects of mastery learning on students’
academic achievements in Nigeria. Journal of Applied Research in Higher Education; Bingley, 10(4),
547–555. https://doi.org/10.1108/JARHE-02-2018-0029
22. Edgerton, E., McKechnie, J., & McEwen, S. (2011). Students’ perceptions of their school environments
and the relationship with educational outcomes. Educational and Child Psychology, 28(1), 33–45.
https://doi.org/10.53841/bpsecp.2011.28.1.33
23. Esmail Sabra, H., Mohammed Hassan, A., & Mostafa Mohammed, H. (2018). Relation between
Students’ Perception of Teaching Styles and Students’ Academic Engagement in South Valley and
Assiut Universities. Egyptian Journal of Health Care, 9(1), 187–204.
https://doi.org/10.21608/ejhc.2018.13953
24. Ezenwoke, O. A., Efobi, U. R., Asaleye, A. J., & Felix, D. E. (2020). The determinants of undergraduate
accounting students’ early participation in professional examinations. Cogent Education, 7(1), 1–18.
https://doi.org/10.1080/2331186X.2020.1818411
25. Fallan, L., & Opstad, L. (2014). Beyond gender performance in accounting: Does personality distinction
matter? Accounting Education, 23(4), 343–361. https://doi.org/10.1080/09639284.2014.930693
26. Foong, C. C., & Liew, P. Y. (2022). The relationships between academic motivation and academic
performance of first-year chemical engineering students. 2433, 020012, 1–9.
27. Goodman, S., Jaffer, T., Keresztesi, M., Mamdani, F., Mokgatle, D., Musariri, M., Pires, J., &
Schlechter, A. (2011). An investigation of the relationship between students’ motivation and academic
performance as mediated by effort. South African Journal of Psychology, 41(3), 373–385.
https://doi.org/10.1177/008124631104100311
28. Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial Least
Squares Structural Equation Modeling (PLS-SEM) Using R (1st ed.). the registered company Springer
Nature Switzerland AG, Gewerbestrasse.
29. Hair, J. F., Matthews, L., Matthews, R., & Sarstedt, M. (2017). PLS-SEM or CB-SEM: updated
guidelines on which method to use. International Journal of Multivariate Data Analysis, 1(2), 90–107.
https://doi.org/10.1504/IJMDA.2017.087624
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XXVI October 2025 | Special Issue on Education
www.rsisinternational.org
Page 9131
30. Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results
of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
31. Hanushek, E. A. (2020). Education production functions. In The Economics of Education: A
Comprehensive Overview (pp. 161–170). Elsevier. https://doi.org/10.1016/B978-0-12-815391-8.00013-
6
32. Henseler, J., Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., Ketchen,
D. J., Hair, J. F., Hult, G. T. M., & Calantone, R. J. (2014). Common Beliefs and Reality About PLS:
Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182–209.
https://doi.org/10.1177/1094428114526928
33. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in
variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–
135. https://doi.org/10.1007/s11747-014-0403-8
34. Husin, N., Che Mat, A., Husin, N. H. R., & Hashim, N. (2023). Decoding Teaching Styles of Language
Lecturers. International Journal of Academic Research in Business and Social Sciences, 13(6), 1914–
1929. https://doi.org/10.6007/ijarbss/v13-i6/17256
35. Iqbal, K., Chaudhry, S. R., Lodhi, H. N., Khaliq, S., Taseer, M., & Saeed, M. (2021). Relationship
between IQ and academic performance of medical students. The Professional Medical Journal, 28(02),
242–246. https://doi.org/10.29309/tpmj/2021.28.02.4348
36. Jepsen, D. M., Varhegyi, M. M., & Teo, S. T. T. (2015). The association between learning styles and
perception of teaching quality. Education and Training, 57(5), 575–587. https://doi.org/10.1108/ET-02-
2014-0005
37. Keerthigha, C., & Singh, S. (2023). The effect of teaching style and academic motivation on student
evaluation of teaching: Insights from social cognition. Frontiers in Psychology, 13(1107375), 1–7.
https://doi.org/10.3389/fpsyg.2022.1107375
38. Kell, H. J., Lubinski, D., & Benbow, C. P. (2013). Who Rises to the Top? Early Indicators.
Psychological Science, 24(5), 648–659. https://doi.org/10.1177/0956797612457784
39. Khumalo, M., & Utete, R. (2023). Factors That Influence Academic Performance of Students: an
Empirical Stud. The Seybold Report, 5(18), 1710–1722. https://doi.org/10.17605/OSF.IO/JCMKU
40. Kock, N., & Hadaya, P. (2016). Minimum sample size estimation in PLS-SEM: The inverse square root
and gamma-exponential methods: Sample size in PLS-based SEM. Information Systems Journal, 28(4),
1–11. https://doi.org/10.1111/isj.12131
41. Kohan, N., Janatolmakan, M., Rezaei, M., & Khatony, A. (2021). Relationship between Learning Styles
and Academic Performance among Virtual Nursing Students: A Cross-Sectional Study. Education
Research International, 2021(4), 1–6. https://doi.org/10.1155/2021/8543052
42. Kumar, A., Singh, N., & Ahuja, N. J. (2017). Learning styles based adaptive intelligent tutoring
systems: Document analysis of articles published between 2001. and 2016. International Journal of
Cognitive Research in Science, Engineering and Education, 5(2), 83–98.
https://doi.org/10.5937/IJCRSEE1702083K
43. Lachowicz, M. J., Preacher, K. J., & Kelley, K. (2018). A novel measure of effect size for mediation
analysis. Psychological Methods, 23(2), 244–261. https://doi.org/10.1037/met0000165
44. Lee, W., Lee, M.-J., & Bong, M. (2014). Testing Interest and Self-Efficacy as Predictors of Academic
Self-Regulation and Achievement. Contemporary Educational Psychology, 39(2), 86–99.
https://doi.org/10.1016/j.cedpsych.2014.02.002
45. Longobardi, C., Pasta, T., Marengo, D., Prino, L. E., & Settanni, M. (2018). Measuring Quality of
Classroom Interactions in Italian Primary School: Structural Validity of the CLASS K–3. The Journal of
Experimental Education, 88(1), 103–122. https://doi.org/10.1080/00220973.2018.1533795
46. Lozano-Blasco, R., Quílez-Robres, A., Usán, P., Salavera, C., & Casanovas-López, R. (2022). Types of
Intelligence and Academic Performance: A Systematic Review and Meta-Analysis. Journal of
Intelligence, 10(4), 1–16. https://doi.org/10.3390/jintelligence10040123
47. Mappadang, A., Khusaini, K., Sinaga, M., & Elizabeth, E. (2022). Academic interest determines the
academic performance of undergraduate accounting students: Multinomial logit evidence. Cogent
Business and Management, 9(1), 1–22. https://doi.org/10.1080/23311975.2022.2101326
48. Mei Ph’Ng, L. (2018). Teaching Styles, Learning Styles and the ESP Classroom. MATEC Web of
Conferences, 150. https://doi.org/10.1051/matecconf/201815005082
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XXVI October 2025 | Special Issue on Education
www.rsisinternational.org
Page 9132
49. Meyer, J., Fleckenstein, J., & Köller, O. (2019). Expectancy Value Interactions and Academic
Achievement: Differential Relationships with Achievement Measures. Contemporary Educational
Psychology, 58, 58–74. https://doi.org/10.1016/j.cedpsych.2019.01.006
50. Mørk, G., Magne, T. A., Carstensen, T., Stigen, L., Åsli, L. A., Gramstad, A., Johnson, S. G., &
Bonsaksen, T. (2020). Associations between learning environment variables and students’ approaches to
studying: A cross-sectional study. BMC Medical Education, 20(1), 1–22.
https://doi.org/10.1186/s12909-020-02033-4
51. Mozaffari, H. R., Janatolmakan, M., Sharifi, R., Ghandinejad, F., Andayeshgar, B., & Khatony, A.
(2020). The relationship between the vark learning styles and academic achievement in dental students.
Advances in Medical Education and Practice, 11(4), 15–19. https://doi.org/10.2147/AMEP.S235002
52. Niet, A. G. van der, Hartman, E., Smith, J., & Visscher, C. (2014). Modeling relationships between
physical fitness, executive functioning, and academic achievement in primary school children.
Psychology of Sport and Exercise, 15(4), 319–325. https://doi.org/10.1016/j.psychsport.2014.02.010
53. Ogbeibu, S., Jabbour, C. J. C., Gaskin, J., Senadjki, A., & Hughes, M. (2021). Leveraging STARA
competencies and green creativity to boost green organisational innovative evidence: A praxis for
sustainable development. Business Strategy and the Environment, 30(5), 2421–2440.
https://doi.org/10.1002/bse.2754
54. Oyewobi, L. O., Bolarin, G., Oladosu, N. T., & Jimoh, R. A. (2020). Influence of stress and coping
strategies on undergraduate students’ performance. Journal of Applied Research in Higher Education,
13(4), 1043–1061. https://doi.org/10.1108/JARHE-03-2020-0066
55. Ozcan, M. (2021). Factors Affecting Students’ Academic Achievement according to the Teachers’
Opinion. Education Reform Journal, 6(1), 1–18. https://doi.org/10.22596/erj2021.06.01.1.18
56. Pérez-López, M. C., & Ibarrondo-Dávila, M. P. (2019). Key variables for academic performance in
university accounting studies. A mediation model. Innovations in Education and Teaching International,
57(3), 1–12. https://doi.org/10.1080/14703297.2019.1620624
57. Ph’ng, L. M., Ming, T. S., & Nambiar, R. M. K. (2016). Match or mismatch: Teaching styles and
learning styles in an ESP classroom. Social Sciences (Pakistan), 11(12), 2977–2982.
https://doi.org/10.3923/sscience.2016.2977.2982
58. Pishghadam, R., Faribi, M., Kolahi Ahari, M., Shadloo, F., Gholami, M. J., & Shayesteh, S. (2022).
Intelligence, emotional intelligence, and emo-sensory intelligence: Which one is a better predictor of
university students’ academic success? Frontiers in Psychology, 13(995988), 1–12.
https://doi.org/10.3389/fpsyg.2022.995988
59. Quilez-Robres, A., González-Andrade, A., Ortega, Z., & Santiago-Ramajo, S. (2021). Intelligence
quotient, short-term memory and study habits as academic achievement predictors of elementary school:
A follow-up study. Studies in Educational Evaluation, 70(3), 1–18.
https://doi.org/10.1016/j.stueduc.2021.101020
60. Redondo-Flórez, L., Ramos-Campo, D. J., & Clemente-Suárez, V. J. (2022). Relationship between
Physical Fitness and Academic Performance in University Students. International Journal of
Environmental Research and Public Health, 19(22), 1–9. https://doi.org/10.3390/ijerph192214750
61. Risher, J., & Hair, J. (2017). The Robustness of PLS Across Disciplines.
https://www.researchgate.net/publication/332354144
62. Sadiku, G. S., & Sylaj, V. (2019). Factors That Influence the Level of the Academic Performance of the
Students. Journal of Social Studies Education Research, 10(3), 17–38.
https://doi.org/10.22373/jms.v20i1.6503
63. Sarstedt, M., Hair, J. F., Nitzl, C., Ringle, C. M., & Howard, M. C. (2020). Beyond a tandem analysis of
SEM and PROCESS: Use of PLS-SEM for mediation analyses! International Journal of Market
Research, 62(3), 1–12. https://doi.org/10.1177/1470785320915686
64. Sarstedt, M., Ringle, C. M., & Hair, J. F. (2017). Partial Least Squares Structural Equation Modeling. In
Handbook of Market Research (Vol. 1, pp. 1–40). Springer International Publishing.
https://doi.org/10.1007/978-3-319-05542-8_15-1
65. Shaari, A. S., Yusoff, N. M., Ghazali, I. M., Osman, R. H., & Dzahir, N. F. M. (2014). The Relationship
between Lecturers’ Teaching Style and Students’ Academic Engagement. Procedia - Social and
Behavioral Sciences, 118, 10–20. https://doi.org/10.1016/j.sbspro.2014.02.002
INTERNATIONAL JOURNAL OF RESEARCH AND INNOVATION IN SOCIAL SCIENCE (IJRISS)
ISSN No. 2454-6186 | DOI: 10.47772/IJRISS | Volume IX Issue XXVI October 2025 | Special Issue on Education
www.rsisinternational.org
Page 9133
66. Sharma, D., & Sharma, S. (2018). International Journal of Advances in Scientific Research Relationship
between motivation and academic achievement QR Code *Correspondence Info. International Journal of
Advances in Scientific Research, 4(1), 1–5. https://doi.org/10.7439/ijasr
67. Shook, S. U. (2016). The Relationship Between Physical Fitness and Academic Achievement in Sixth
Grade Students. Walden University.
68. Sivrikaya, A. H. (2019). The Relationship between Academic Motivation and Academic Achievement
of the Students. Asian Journal of Education and Training, 5(2), 309–315.
https://doi.org/10.20448/journal.522.2019.52.309.315
69. Sothan, S. (2019). The determinants of academic performance: Evidence from a Cambodian University.
Studies in Higher Education, 44(11), 2096–2111. https://doi.org/10.1080/03075079. 2018.1496408
70. Steinmayr, R., Weidinger, A. F., & Wigfield, A. (2018). Does students’ grit predict their school
achievement above and beyond their personality, motivation, and engagement? Contemporary
Educational Psychology, 53, 106–122. https://doi.org/10.1016/j.cedpsych.2018.02.004
71. Sternberg, R. J. (2019). A theory of adaptive intelligence and its relation to general intelligence. Journal
of Intelligence, 7(4), 2–17. https://doi.org/10.3390/jintelligence7040023
72. Tang, C. W., Shi, M. J., & Gusman, A. de. (2022). Lecturer teaching styles and student learning
involvement in large classes: a Taiwan case study. Asia Pacific Journal of Education , 42(3), 447–463.
73. Thu Ha, N. T. (2021). Effects of Learning Style on Students Achievement: Experimental Research.
Linguistics and Culture Review, 5(3), 329–339. https://doi.org/10.37028/lingcure.v5nS3.1515
74. Vidyakala, K., Nandhini, P., Nithyakala, & Deepa, J. (2019). A Conceptual Study on Relationship
Between Learning Styles and Academic Performance. International Journal of Management, 9(3), 260–
266. https://ssrn.com/abstract=3616819
75. Wetzels, M., Odekerken-Schröder, G., & Van Oppen, C. (2009). Using PLS path modeling for assessing
hierarchical construct models: Guidelines and empirical illustration. MIS Quarterly: Management
Information Systems, 33(1), 177–196. https://doi.org/10.2307/20650284
76. Yarin, A. J., Encalada, I. A., Elias, J. W., Surichaqui, A. A., Sulca, R. E., & Pozo, F. (2022).
Relationship between Motivation and Academic Performance in Peruvian Undergraduate Students in the
Subject Mathematics. Education Research International, 2022(1), 1–11.
https://doi.org/10.1155/2022/3667076
77. Zhoc, K. C. H., Chung, T. S. H., & King, R. B. (2018). Emotional intelligence (EI) and self-directed
learning: Examining their relation and contribution to better student learning outcomes in higher
education. British Educational Research Journal, 44(6), 982–1004. https://doi.org/10.1002/berj.3472
