Volume 13 - Issue 73
/ January 2024
41
http:// www.amazoniainvestiga.info ISSN 2322- 6307
DOI: https://doi.org/10.34069/AI/2024.73.01.4
How to Cite:
Khan, I. (2024). Factors influencing students’ academic achievement: evidence from University of Ha’il Kingdom of Saudi
Arabia. Amazonia Investiga, 13(73), 41-55. https://doi.org/10.34069/AI/2024.73.01.4
Factors influencing students’ academic achievement: evidence from
University of Ha’il Kingdom of Saudi Arabia
Received: December 28, 2023 Accepted: January 28, 2024
Written by:
Imran Khan1
https://orcid.org/0000-0002-8560-6022
Abstract
The current study's goal is to determine the effect of "student interest," "perceived self-efficacy," and "learning
motivation" on undergraduate students' CGPA. The present investigation employed a quantitative methodology,
utilizing a cross-sectional survey delivered through an online Google Form that participants self-administered.
The current study's target demographic was undergraduate students at a public university. In this survey, 230
undergraduate students took part. The variable combination predicted approximately 39.6% of the overall
variance in predicting the CGPA. The predicted regression model in the study was significant (F(3,226 = 50.960,
p 0.001), and it discovered that other than "students' interest," only two factors significantly predicted the
outcome variable CGPA. However, "student interest" has a positive but negligible effect on the CGPA. It is
recommended that teachers use effective classroom strategies to assist students in raising their interest, learning
motivation, and self-efficacy to accelerate their academic achievement.
Keywords: students’ interest, self-efficacy, learning motivation, CGPA, undergraduate students, Ha’il
University.
230
.639 (F(3,226 = 50.960, p 0.00)
Introduction
Theoretical background
Until recently, the policy on education has not
prioritized encouraging student learning as well
as very precisely, how to stimulate and sustain
1
Department of English, College of Arts, University of Ha’il, Saudi Arabia. WoS Researcher ID: KBC-1319-2024
their interest in learning (Renninger & Hidi,
2020). Besides, taking an interest in what one is
doing improves comprehension (Hagay &
Baram-Tsabari, 2011). The growth of interest
correlates with the capacity to maintain focus,
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plan and achieve objectives efficiently, apply
learning techniques to manage behavior, feel
confident, and make innovative contributions
(Hidi, 1995; McDaniel, et al., 2000;
Harackiewicz et al., 2008; Bernacki &
Walkington, 2018; Sansone et al., 2015; Lee, et
al., 2014; Izard & Ackerman, 2000). Within the
wider context of education, learners have a
network or framework of particular interests, a
few directly tied to instructional goals, and others
hostile to classroom learning (Ainley et al.,
2002). It has been extensively reported in the
literature how researchers have reintroduced the
idea of interest after years of neglect. (Hidi,
1990; Krapp, 1999; Krapp, et al., 1992).
Furthermore, the opinions people have about
their ability to perform at specific capacities and
exercise power over situations that affect their
lives are referred to as perceived self-efficacy
(Bandura, 1994). Self-efficacy is still a useful
term since studies have indicated that
a substantial degree of self-efficacy is linked to
an optimistic self-perception, the use of
advanced learning techniques, success standards,
and persistence in a task (Puzziferro, 2008; Wang
& Wu, 2008). Moreover, self-efficacy is the
conviction that one can plan and carry out the
necessary actions to achieve a desired outcome
(Bandura, 1997). An absence of self-efficacy is
also linked to a poor perception of oneself, and
an aversion to taking on new challenges (Hsieh
et al., 2008). According to Demirtas (2010),
achievement among learners is demonstrated by
the actions, expertise, and abilities that all
students develop in learning contexts. It is also
reflected in their educational results (Demirtas,
2010). Numerous studies on students' academic
achievement have been undertaken (e.g.,
Demirtas 2010; Flashman, 2012; Lindholm-
Leary, & Borsato, 2006; Wang & Wu, 2008).
Individual variations in learning capacity and
willingness to learn have long been thought to be
major antecedents of learning and training
performance (Campbell, 1989; Goldstein, 1993;
Noe, 1986; Noe & Schmitt, 1986).
Review of the related literature
Students’ interest
It appears to have consistently shown that
interest, a concept with both cognitive and
emotional components, influences learning. It
has been seen to impact students' self-control and
focus (Ainley et al., 2002; & Hidi & Ainley,
2008). One definition of individual interest is a
reasonably persistent inclination to pay attention
to particular events and occurrences and get
involved in particular pursuits (Krapp et al.,
1992; Renninger, 1992; Renninger, 2000). The
level of excellence of a person's involvement in
projects, activities, and assignments is improved
by interest growth. Students with minimal or no
experience might not be required to choose their
courses, as interest is necessary for them to reach
a well-informed selection (Renninger & Hidi,
2020). Hidi and Renninger (2006) define the
initial spark of interest as enabling interaction,
which, if sustained, may continue to expand and
expand as time passes. This is reflected in their
four-phase model of interest building. According
to Ainley (1998), having a broad interest in
learning is a defining attitude to tackling
unfamiliar, unclear, or perplexing phenomena to
comprehend them. This kind of interest may
entail simultaneously extending one's current
understanding and acquiring new information.
Moreover, Ainley's (1998) research discovered, a
variety of favorable views on education were
linked to an individual's overall interest in
learning and academic achievement. The
following represent a few instances of techniques
for piquing and sustaining attention that can take
into account variations in learners’ interest:
i) providing current content to students by use of
unique, unexpected, or challenging assignment
aspects (Hidi & Baird, 1986; Nieswandt &
Horowitz, 2015); ii) allowing students to
collaborate directly on unrestricted assignments,
capitalizing on their interest in the interpersonal
aspects of collaborative tasks (Knogler, et al.,
2015; Mitchell, 1993); iii) putting students'
current interests within the context of texts as
well as challenges to personalize the material
(Bernacki & Walkington, 2018). Numerous
studies on students' interest have been
undertaken (Ainley, 1998; Ainley et al., 2002;
Xu et al., 2012; Crouch et al., 2018; Rotgans &
Schmidt, 2011).
Perceived self-efficacy
Perceptions of one's ability to plan and carry out
the actions necessary to achieve certain goals are
called self-efficacy (Bandura, 1997). Self-
efficacy has a significant influence on students'
academic achievement because students with
poorer levels of self-efficacy find it harder to
persevere through more demanding, tough
assignments (Bandura, 1996; De Clercq et al.,
2011; Richardson et al., 2012). In an unfavorable
environment, students struggle with educational
adjustment in university, which has a detrimental
influence on their educational advancement
(Bailey & Phillips, 2016; Pascarella & Terenzini,
2005). Self-efficacy refers to a person's views
that are developed through their daily
Khan, I. / Volume 13 - Issue 73: 41-55 / January, 2024
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interactions. These beliefs impact the
motivational, intellectual, and emotional
reactions that people have when acquiring and
growing (Bandura, 1996). Academic self-
efficacy is essential to all aspects of a student's
educational process, acting as a critical mediator
in how learners act (Schunk & Mullen, 2012).
Several research findings suggest that those with
strong academic self-efficacy are more likely to
exert significant effort when accomplishing
academic assignments. On the other hand,
individuals who have poor academic self-
efficacy typically avoid taking on academic
issues that they believe are beyond their reach
(Britner & Pajares, 2006; Kiran & Sungur, 2012).
Learning motivation
Learning motivation is paying attention to and
absorb the knowledge offered in a course of
study for one's professional development (Noe,
1986). Likewise, it is well-recognized how
people's learning motivation correlates to a
variety of cognitive effects, notably, post-
learning motivation, satisfaction as well as
responses to instruction, and anxiousness
(Colquitt et al., 2000). Cole et al. (2004) predict
that the favorable association between class-
specific motivation to acquire knowledge and
emotional effects will be best if resilience is
higher. Within such conditions, students are
likely to have greater demands on themselves
academically, partially because they are
determined, feel effective, and view their current
situation including their capacity to deal with it
as less threatening. They go on to say that
students who have been stimulated by
educational difficulties are anticipated to stay
driven, feel more cheerful rather than sad, and to
respond positively towards their curriculum and
teachers (Cole et al., 2004). Considering learning
motivation seems changeable and may alter over
a while (Noe, 1986), individuals' degree of
learning motivation might fluctuate over a
semester. Students' motivation for academic
achievement may improve, diminish, or remain
unchanged (Cole et al., 2004).
Previous studies and hypotheses development
Robbi et al. (2020) conducted a quantitative
study on learning motivation on learning
achievement in Indonesia with a sample of 224
students. Their study showed that students’
success is significantly influenced by learning
motivation. Similarly, Colquitt and Simmering
(1998) performed a six-week longitudinal
research on goal-setting and motivation to learn
using 103 samples. They observed that diligence
and ‘learning orientation’ were associated with
motivation to learn before as well as following
obtaining ‘performance feedback’, whereas
‘performance orientation’ was negatively
associated with willingness to study equally
before and following obtaining ‘performance
feedback’. The Investment Model Scale was
established by Rusbult et al. (1998) to
assess several factors that are important for
comprehending how relationships function. With
the process of measuring these variables, the
scale gives an in-depth structure for assessing the
stability and strength of interactions.
Feng (2013) studied on 109 Taiwanese
undergraduate students. Their findings indicate
that learning motivation is an important aspect of
acquiring English as a foreign language, while
there are a few differences between genders in
students' learning motivations. Moreover,
Huseinović (2024) evaluated the influence of
gaming on student motivation and academic
performance at higher education institutions. The
study's findings show that gaming tactics have a
substantial influence on students' motivation and
also on how well they do in EFL classes and their
academic achievement. In addition to the
conventional behavioral, emotional, and
cognitive dimensions, Reeve and Tseng (2011)
investigate the idea of agency in students'
participation in learning events and propose it as
a fourth dimension. Their study investigates how
agency, defined as students' active involvement
in the learning process, influences overall
engagement and academic achievement.
Through empirical research and theoretical
analysis, the authors assert that fostering agency
is crucial for promoting deeper and more
meaningful learning experiences.
Asvio et al. (2017) carried out a study to discover
the effects of students' learning motivation on
their academic accomplishment. They conducted
this quantitative study on a sample of 129
students. Their findings showed that students'
learning motivation had a significant favorable
effect on their learning accomplishment. Zhao et
al. (2022) investigated the impact of various
learning tactics on learning motivation. Their
study revealed that learning styles had a
considerable influence on ‘deep motivation’.
Furthermore, Muthik et al. (2022) determined the
impact of students' learning motivation on
academic results utilizing the reciprocal
teaching-learning framework. Their findings
indicate that the use of reciprocal teaching-
learning strategies can enhance student
achievement by inspiring students to learn.
Similarly, the association between middle school
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pupils' academic achievement and their self-
efficacy attribution is examined by Kairong et al.
(1999). Their study explores the relationship
between students' self-perceptions and academic
success. It is likely that the researchers looked at
how students' self-perceptions of their skills
affect their drive, work ethic, and academic
performance.
Jiao et al. (2022) research looked into the
learning motivation of Chinese ethnic
backgrounds university students. This study
included a sample of 776 undergraduates
representing three ethnically represented
universities. The research revealed four distinct
forms of English learning motivation: "intrinsic
interest", "learning situation", "personal
development", and "international
communication". Findings showed that learning
context motivation had a considerably negative
effect on English proficiency, but intrinsic
interest motivation showed a significantly
positive effect. Similarly, Munawaroh et al.
(2022) conducted a study with 129 learners from
Indonesia's Economics department. They sought
to find out how Koschmann, Myers, and
Barrows’ (1993) e-PBL framework affected
motivation among pupils, interest, and success.
They verified their hypotheses using the path
analysis approach. They discovered the e-PBL
approach assists students in solving and
exploring their analytical abilities while also
piquing their interest in tackling problems with
learning.
Renninger and Hidi (2020) suggested a four-
stage model for student interest development.
They discovered that transformation in each
stage of interest growth by an action of activating
that drives seeking information, growing
knowledge, and fostering appreciation in
students. Besides, Ainley et al. (2002) explored
the role of computer tasks in mediating students'
interest and learning. Researchers looked into
whether personal context-specific elements
influence subject interest in sentence learning.
According to the study's findings, the most robust
model relating subject interest and learning
stated that subject interest was associated with a
psychological reaction, the impact ultimately
then linked to text persistence, and perseverance
contributed to academic achievement. Wilkins et
al. (2016) look at how dedicated students are to
their studies, how well they perform
academically, and how satisfied they are with
their whole educational experience. Study results
indicate that students' involvement and
achievement in higher education are positively
impacted by their sense of loyalty and belonging
in both social and organizational environments.
Casanova et al. (2024) studied academic
performance determinants in 447 undergraduate
students. For demographic factors, the results
reveal statistically significant pathways.
Academic engagement and self-efficacy had a
favorable, substantial, and statistically
noteworthy correlation. A recent study
conducted by Chen et al. (2023). They explored
the associations between career personality,
academic self-efficacy, and learning
participation among students studying tourism.
According to the findings, there is no substantial
relationship between students' occupational
cognitive abilities and educational involvement.
According to the previous evaluation of the
literature, the bulk of research has explored the
interests of learners, learning motivation, and
self-efficacy, with relatively few studies
investigating the influence of the three
antecedents on the CGPA. Furthermore, the
influence of these factors has not been
investigated in the Kingdom of Saudi Arabia. As
a result, the current study aims to answer the
following research question in light of previous
studies and empirical findings:
Research hypotheses
The literature that has been discussed and the
evidence from empirical studies provide the basis
for the following hypotheses:
H1. There is a positive impact of undergraduate
students’ interest on their CGPA score
H1a. There is a positive impact of male
undergraduate student’s interest on their
cumulative grade point average score
H1b. There is a positive impact of female
undergraduate student’s interest on their
cumulative grade point average score
H2. There is a positive impact of undergraduate
students’ perceived self-efficacy on their
cumulative grade point average score
H2a. There is a positive impact of male
undergraduate students’ perceived self-efficacy
on their cumulative grade point average score
H2b. There is a positive impact of female
undergraduate students’ perceived self-efficacy
on their cumulative grade point average score
H3. There is a positive impact of undergraduate
students’ learning motivation on their cumulative
grade point average score
H3a. There is a positive impact of male
undergraduate students’ learning motivation on
their cumulative grade point average score
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H3b. There is a positive impact of female
undergraduate students’ learning motivation on
their cumulative grade point average score
Methodology
The current study was explanatory, and the
hypothesized model included three variables:
"student interest," "perceived self-efficacy," and
"learning motivation." The present study used the
CGPA score as a continuous dependent variable.
These three dimensions are used to see their
impact on CGPA score. Based on the previous
studies, Figure 1 illustrates the link between
these three variables and the outcome variable.
Based on the foregoing explanation, the
following regression model is used in the present
investigation:
CGPA score = αo + β1 (SI) + β2 (PSE) + β3 (LM)
+ ε
Figure 1. Research Model of the Study
Research design
Consequently, an online questionnaire that
participants self-administered via Google Form
was used to conduct the cross-sectional survey.
Through the Blackboard network, an email was
sent to the students who participated asking them
to click on a link that led to the intended
questionnaire. The present study's sample was
derived utilizing non-random sampling strategies
that included purposive and convenience
sampling. Two hundred and eighty nine
undergraduate students from one public
university participated in the study.
Measures
Independent variables
The "student interest" among the learners was
measured using seven items that were obtained
from (Mazer, 2012). This construct was formed
using a six-point Likert scale, ranging from
"never" (1) to "every time" (7). During
instrument piloting, the construct's Cronbach
alpha was (n = 30; α = 0.929). Students’
“perceived self-efficacy” was measured using
eight items adopted from (Chen et al., 2001). A
five-point Likert scale, ranging from "strongly
disagree" (1) to "strongly agree" (5), was used to
develop this construct. In the pilot study, this
construct's Cronbach alpha was (n=30; α =
0.885). Six items that were taken from (Noe &
Schmitt, 1986; Cole et al., 2004) were used to
measure the "learning motivation" of the
students. This construct was developed using a
six-point Likert scale, which goes from "strongly
disagree" (1) to "strongly agree" (6). The
Cronbach alpha for this construct during
instrument piloting was (n=30; α = 0.857).
Cumulative grade point average (dependent
variable)
I am especially intrigued about the impact of
students' interest, perceived self-efficacy, and
learning motivation on their CGPA. The self-
reported average score in all subjects taught in a
university program determine educational
achievement. A student's CGPA is calculated by
multiplying their cumulative completed hours
(i.e., hours of credit for which they received a
grade) by the total amount of hours in their
current semester and the grade values of the
subjects they took. It varies throughout each
respondent's higher education. The cumulative
grade point average CGPA appears as a
continuous measure. In essence, a (4.0) GPA, or
an (A+ = 95-100; A = 94-90) average across all
subjects, is the highest possible score. An
average of (3.0) could correspond to a (B+ = 89-
85; B = 84-80), (2.0) to a (C+ = 79-75; C = 74-
70), (1.0) to a (D+ 69-65; D 64-60), and (0.0) to
an (F = 59-0). I coded employing a seven-point
scale in SPSS and tried out stringent cut-offs (1 =
< 2.5, 2 = 2.51-2.75, 3 = 2.76-3.0, 4 = 3.01-3.25,
5 = 3.26-3.50, 6 = 3.51-3.75, 7 = 3.76 & above).
Several earlier empirical studies have included
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CGPA as a dependent variable (Flashman, 2012;
Rosli 2012, Nurudeen et al., 2023).
Results and discussion
Techniques and procedures for studying data
Descriptive along with inferential statistics were
performed using the 23rd release of the
“Statistical Package for Social Sciences” (SPSS).
Initially, descriptive data were used to determine
their mean, standard deviations, frequency range,
and percent. The reliability statistics of the
loaded items and the “Pearson correlation” were
examined. The technique of regression analysis
was then employed to evaluate the model's ability
to predict its hypothesis. To assess the variation
in means and variances with regard to the gender
variable, group analysis and an independent t-test
were also carried out.
Table 1.
Descriptive Statistics
M
SD
Demographics
f
%
Cumulative %
Gender
1.51
0.501
Male
113
49.10
49.10
Age
1.71
0.516
Female
117
50.90
100.00
College
1.99
1.135
18-21 years
73
31.70
31.70
CGPA
5.57
0.577
22-25 years
150
65.20
97.00
26-29 years
7
3.00
100.00
College of Arts
111
48.30
48.30
College of Applied Medical Sciences
49
21.30
69.60
College of Business Administration
32
13.90
83.50
College of Community
38
16.50
100.00
2.76-3.0
1
0.40
0.40
3.01-3.25
7
3.00
3.50
3.26-3.50
81
35.20
38.70
3.51-3.75
141
61.30
100.00
Total
n = 230
100.00
The sample population's major variables and
demographic features are summarized in Table
1's descriptive statistics. Values for the mean (M)
and standard deviation (SD) of continuous
variables, including age and CGPA, are given.
Additionally, frequencies (f) and percentages (%)
are shown for qualitative characteristics like
gender and college affiliation. With 113 male
(49.10%) and 117 female (50.90%), the gender
distribution is fairly balanced, according to the
data. The age distribution of the participants
reveals that the majority are between the ages of
22 and 25 (65.20%), with a lesser percentage
being between the ages of 26 and 29 (3.00%).
The College of Arts has the highest frequency
(48.30%) among the colleges included in the
statistics regarding affiliation.
Table 2.
Pilot study
Sr. #
Variables
No. of
Items
Cronbach's
Coefficient
Alpha
Cronbach Alpha of
21 items
1
Students' Interest (SI)
7
0.929
2
Perceived Self-Efficacy (PSE)
8
0.885
0.835
3
Learning Motivation (LM)
6
0.857
Note: (n= 30)
21
Research instrument and piloting
The pilot study's results are displayed in Table 2.
The self-administered questionnaire consists of
21 items. After data cleaning, the final sample
size for the study consisted of 230 out of the 289
total respondents. Before the main investigation,
a pilot study with thirty respondents was carried
out. The respondents from the pilot study were
not included in the main study.
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Table 3.
Means, standard deviations, and inter-correlations between independent variables (n = 230)
M
SD
1
2
3
Students' Interest (SI)
1.0510
0.1501
1
Perceived Self-Efficacy (PSE)
0.7856
0.0717
.456**
1
Learning Motivation (LM)
1.3962
0.1577
.510**
.654**
1
Notes: ** p < 0.01(2 - tailed); *p < 0.05(2 - tailed)
The means (M), standard deviations (SD), and
intercorrelations between the independent
variables are displayed in Table 3. The
correlation matrix contains the intercorrelations
coefficients between the variables. In particular,
there is a significant correlation between
Students' Interest and both Perceived Self-
Efficacy (r = 0.456, p < 0.01) and Learning
Motivation (r = 0.510, p < 0.01), and a positive
correlation between Perceived Self-Efficacy and
Learning Motivation (r = 0.654, p < 0.01). Strong
relationships between the variables are suggested
by these statistically significant correlations.
Table 4.
Reliability before factors loading
Sr. #
Variables
No. of items
Individual Alpha
Alpha of 21 items
1
Students' Interest (SI)
7
0.924
2
Perceived Self-Efficacy (PSE)
8
0.924
0.937
3
Learning Motivation (LM)
6
0.905
Total Likert scale items
21
Note: (n= 230)
Reliability data for three variables are shown in
Table 4 prior to factor loading. The table
provides the reliability coefficient (calculated
using Cronbach's alpha) and the number of
elements that make up the scale for each variable.
The reliability coefficients for learning
motivation, perceived self-efficacy, and students'
interest are 0.905, 0.924, and 0.924, respectively.
High internal consistency within each variable's
scale is indicated by these reliability coefficients,
indicating that the items accurately assess the
underlying components.
Table 5.
Reliability after factors loading
Sr. #
Variables
No. of items
Individual Alpha
Alpha of 16 items
1
Students' Interest (SI)
6
0.922
2
Perceived Self-Efficacy (PSE)
6
0.898
0.921
3
Learning Motivation (LM)
4
0.892
Total Likert scale items
16
Note: (n= 230)
After factor loading, Table 5 displays reliability
data for the three variables that make up the scale
and the reliability coefficient, which is calculated
using Cronbach's alpha. Following factor
loading, the reliability coefficients for learning
motivation, perceived self-efficacy, and students'
interest are 0.892, 0.898, and 0.922, respectively.
These coefficients show strong internal
consistency within the scale of each variable.
Table 6.
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
0.903
Bartlett's Test of Sphericity
Approx. Chi-Square
2938.010
df
120
Sig.
0.000
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Table 6 displays Bartlett's Test of Sphericity and
the Kaiser-Meyer-Olkin (KMO) measure of
sample adequacy. The KMO measure, which
indicates the percentage of variation across
variables that might be shared, comes back with
a value of 0.903, over the cutoff of 0.6 and
indicating a high degree of factor analysis
appropriateness (Kaiser, 1974). The correlation
matrix's divergence from the identity matrix is
examined using Bartlett's Test of Sphericity,
which produces a significant chi-square value of
2938.010 having 120 degrees of freedom and a
significance level of 0.000, suggesting
significant differences. This suggests that the
variables have sufficient correlation, hence
validating the suitability of the dataset for factor
analysis.
Table 7.
Rotated Component Matrix
Factors Loading Items
Components
(SI)
(PSE)
(LM)
SI2 I understand the course material.
0.857
SI6 I feel like I am learning topics covered in the course.
0.838
SI5 I realize what is expected of me.
0.825
SI4 The information in the course is useful.
0.824
SI3 I can understand the flow of ideas
0.816
SI7 The information covered in the course is making me more
knowledgeable.
0.719
PSE8 Even when things are tough, I can perform quite well.
0.837
PSE6 I am confident that I can perform effectively on many different tasks.
0.791
PSE4 I believe I can succeed at most any endeavor to which I set my mind.
0.781
PSE5 I will be able to successfully overcome many challenges.
0.750
PSE7 Compared to other people, I can do most tasks very well.
0.687
PSE2 When facing difficult tasks, I am certain that I will accomplish them.
0.684
LM5 I try my best to study the course material.
0.839
LM3 I spend a lot of time for my study.
0.785
LM6 Overall, my learning motivation is very high.
0.745
LM4 Investment in studying the course material is my first priority.
0.730
Eigen values
4.460
3.991
3.359
% of Variance explained
27.873
24.946
20.996
Cumulative % of variance explained
27.873
52.819
73.815
Cronbach's α
0.922
0.898
0.892
Notes: Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization;
Rotation converged in 6 iterations; Factor loadings less than |0.40| were
omitted.
SI, Students' interest; PSE, Perceived self-efficacy; LM, Learning motivation
Exploratory factor analysis
In order to obtain the three desired factors, I have
utilized the "Principal components" factoring
option in SPSS 23. To maintain clarity, factor
loadings smaller than |0.40| were removed from
Table 7. Moreover, Table 7 demonstrates that all
loaded items in EFA were more than |0.67|,
indicating a highly robust convergent and
construct validity (Cooper et al., 2007; Field,
2009; Hair et al., 1998; Hair et al., 2009;
Tharenou et al., 2007). In the rotated component
matrix table, the factor loadings obtained via a
principal component analysis with Varimax
rotation (Kaiser normalization) are displayed.
The purpose of this research was to determine the
underlying variables or dimensions that were
associated with learning motivation (LM),
perceived self-efficacy (PSE), and students’
interest (SI). As seen, Factor 1 (SI) shows high
loadings (range 0.719 to 0.857). Similarly, Factor
2 (PSE) shows a significant loading of items
associated with Perceived Self-Efficacy, with
loadings ranging from 0.684 to 0.837. Finally,
Factor 3 (LM) shows a substantial loading of
items, with loadings ranging from 0.730 to 0.839.
Finally table 7 shows the dependability and
explanatory power of the extracted components.
The eigenvalues demonstrate the amount of
variation explained by each component obtained
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in the study. In this instance, the initial
component, possessing an eigenvalue of 4.460, is
capable of elucidating a substantial portion of the
overall variation in the data. Likewise, the second
and third components possess eigenvalues of
3.991 and 3.359, respectively, signifying their
noteworthy contributions to the explained
variance. Examining the proportion of variation
explained by each element can provide further
insights. For instance, the first factor represents
27.873% of the variation, the second factor
represents 24.946%, and the third factor
represents 20.996%.
Table 8.
Means, standard deviations, and correlations among all variables (n = 230)
M
SD
1
2
3
4
Students' Interest (SI)
1.0510
0.1501
1
Perceived Self-Efficacy (PSE)
0.7856
0.0717
.456**
1
Learning Motivation (LM)
1.3962
0.1577
.510**
.654**
1
CGPA (dependent variable)
5.5700
0.577
0.369**
0.600**
.546**
1
Notes: ** p < 0.01(2 - tailed); *p < 0.05(2 - tailed)
The means, standard deviations, and correlations
between the variables are shown in table 8.
Numerous noteworthy conclusions are drawn
from the correlations' analysis. First off, there are
positive connections between Students' Interest
and CGPA (r = 0.369), Learning Motivation (r =
0.510), and Perceived Self-Efficacy (r = 0.456),
all of which are considered significant at p <
0.01. This suggests that increased perceived self-
efficacy, learning to motivation, and students
interest are significantly linked to their academic
performance. Furthermore, a robust positive
association is shown between Perceived Self-
Efficacy and both Learning Motivation (r =
0.654) and CGPA (r = 0.600), with a statistical
significance of p < 0.01. This shows that
motivated students tend to do better academically
and earn higher grades when they believe they
are capable of doing so. Furthermore, there is a
positive association (r = 0.546) between learning
motivation and CGPA, suggesting that students
who are more motivated tend to perform better
academically. Overall, as shown via their
significant correlations with CGPA, these results
highlight the significance of students' motivation,
interest, and perceived self-efficacy in predicting
academic achievement.
Table 9.
Testing hypotheses with entry method-based simultaneous regression análisis
Hyp.
Predictors
β
SE
t-stat.
Sig.
VIF
Relationship
observed
Remarks
(Constant)
1.477
0.336
4.393
0.000
H1
SI
0.208
0.234
0.891
0.374
1.401
positive
Not Supported
H2
PSE
3.318
0.556
5.965
0.000***
1.811
positive
Supported
H3
LM
0.911
0.261
3.482
0.001***
1.937
positive
Supported
DV: CGPA
Notes: F(3, 226) = 50.960, (p<.001); Adj R² = 0.396 *p < 0.05
Hypothesis testing and regression análisis
The findings of testing the hypotheses using
simultaneous multiple linear regression analysis
for predicting "CGPA" (dependent variable) are
shown in Table 9. The combination of variables
predicted approximately 39.6% of the total
variance in predicting the CGPA The study's
predicted regression model was significant
(F(3,226 = 50.960, p < 0.001), and it found that,
aside from "students' interest," only two factors
substantially predicted the outcome variable
CGPA. The link between the predictors and the
dependent variable is represented by the value of
β. It is clear from Table 9 that all three of the
predictors have positive β values. This proves
that in a model with two variables, "perceived
self-efficacy" and "learning motivation" have a
positive significant impact on CGPA. On the
other hand, "student interest" has a positive but
insignificant impact on the CGPA. The variables
used for prediction do not exhibit
multicollinearity since their variance inflation
factor (VIF) is lower than 10. If the VIF is more
than 10, multicollinearity has been observed
(Woodrow, 2014). The coefficients of parameter
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www.amazoniainvestiga.info ISSN 2322- 6307
estimations indicate that "perceived self-
efficacy" (3.318; t 5.965, p < 0.05) and "learning
motivation" (0.911; t 3.482, p < 0.05) have a
statistically significant impact on CGPA. Thus,
two hypotheses (H2 & H3) were supported.
However, "students' interest" (0.208; t 0.891, p <
0.05) had a statistically insignificant impact
when predicting CGPA, hence (H1) was not
supported. The regression equation to predict
CGPA is displayed in the following equation:
CGPA score = 1.477 + 0.208 (SI) + 3.318 (PSE)
+ 0.911 LM
Table 10.
Multiple regression (male model); Dependent variable: CGPA
Hyp.
Predictors
β
SE
t-stat.
Sig.
VIF
Relationship
observed
Remarks
(Constant)
2.26
0.585
3.863
0.000
H1a
SI
0.244
0.502
0.487
0.627
1.616
positive
Not Supported
H2a
PSE
2.772
1.042
2.661
0.009**
2.059
positive
Supported
H3a
LM
0.533
0.442
1.205
0.231
2.173
positive
Not Supported
Notes: F(3, 109) = 10.475, (p<.001); Adj R² = 0.202 *p < 0.05
The findings of testing the hypotheses using
simultaneous multiple linear regression analysis
for predicting CGPA with respect to male gender
are shown in Table 10. The combination of
variables predicted approximately 20.2% of the
total variance in predicting the CGPA. The
coefficients of parameter estimations indicate
that "perceived self-efficacy" (2.722; t 2.661, p <
0.05) has a statistically significant impact on
CGPA. Thus, hypothesis (H2a) was supported.
However, male “students’ interest” (0.244; t
0.487, p < 0.05) and “learning motivation”
(0.533; t 1.205, p < 0.05) had a statistically
insignificant impact when predicting CGPA,
hence (H1a & H3a) were not supported.
Table 11.
Multiple regression (female model); Dependent variable: CGPA
Hyp.
Predictors
β
SE
t-stat.
Sig.
VIF
Relationship
observed
Remarks
(Constant)
0.78
0.313
2.491
0.014
H1b
SI
0.364
0.192
1.894
0.061
1.336
positive
Not Supported
H2b
PSE
3.385
0.485
6.982
0.000***
1.676
positive
Supported
H3b
LM
1.347
0.25
5.394
0.000***
1.807
positive
Supported
Notes: F(3, 113) = 84.024, (p<.001); Adj R² = 0.682 *p < 0.05
Table 11 displays the findings of evaluating the
hypotheses for predicting CGPA with regard to
female gender using simultaneous multiple linear
regression analysis. The combination of
variables predicted approximately 68.2% of the
total variance in predicting the CGPA. The
coefficients of parameter estimations indicate
that female students’ "perceived self-efficacy"
(3.385; t 6.982, p < 0.05) and “learning
motivation” (1.347; t 5.394, p < 0.05) have a
statistically significant impact on CGPA. Thus,
hypotheses (H2b & H3b) were supported.
However, female student’ interest (0.364; t
1.894, p < 0.05) had a statistically insignificant
impact when predicting CGPA, hence (H1b) was
not supported.
Table 12.
Group Statistics
Gender
n
M
SD
Std. Error Mean
CGPA
Male
113
5.43
0.581
0.055
Female
117
5.71
0.542
0.05
Group statistics for CGPA according to gender
are shown in table 12. Each gender group's
standard error of the mean, standard deviation,
mean, and number of participants are shown in
table 12. The mean CGPA for male (n = 113) is
5.43, with a standard deviation of 0.581 and a
mean standard error of 0.055. The mean CGPA
for females (n=117) is 5.71, with a standard error
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of the mean of 0.05 and a significantly smaller
standard deviation of 0.542. This table compares
male and female participants' CGPAs, providing
insight into potential gender variations in
academic achievement within the sample group.
Table 13.
Independent Sample Test: Mean comparison of CGPA score of male and female
Dependent Variable
Male
Female
CGPA score
M SD
M SD
t( 225.612)
p
Cohen's d
5.43 0.581
5.71 0.542
3.72
0.000
0.498
An independent sample t-test was also conducted
to compare the CGPA score for male and female
respondents as shown in table 13. There were
significant differences (t (df) = 225.612, p =
0.000) in the scores with mean score for male (M
= 5.43, SD = 0.581) was lower than female (M =
5.71, SD = 0.542). The magnitude of the
differences in the means (0.276, 95% CI: 0.422
to 0.13) was significant. Hence, null hypothesis
was rejected. The value of Cohen’s d was 0.498
(< 0.50) which indicated medium effect size
(Cohen, 1988).
Conclusions
This research aimed to examine the correlation
and effect of three factors: "learning motivation,"
"perceived self-efficacy," and "students' interest"
on the CGPA of undergraduate students. Data for
the study were gathered from 230 undergraduate
students at a public university. The data was
analyzed using descriptive statistics, exploratory
factor analysis, linear regression, zero-order
correlation, and an independent t-test. The
variable combination predicted approximately
39.6% of the overall variance in predicting the
CGPA. The study's predicted regression model
was significant (F(3,226 = 50.960, p 0.001), and
it indicated that, aside from "students' interest,"
only two factors significantly predicted the
outcome variable CGPA to the current study's
findings, the "students' interest" variable did not
affect undergraduate students' CGPA.
Furthermore, regression was performed
separately on male and female students, and it
was discovered that the "students' interest"
variable has no significant influence on their
CGPA. According to the findings, there is a
positive and statistically significant correlation
between undergraduate students' "learning
motivation," "perceived self-efficacy," and
"students' interest."
Practical implications of the study
It is a serious concern at higher education level
that students’ interest vary due to wide range of
learning settings. Teachers and authorities may
enhance academic learning for all students by
fostering the development of interests.
Cultivating interest amongst students preserves
involvement, improves learning, and optimizes
academic achievement. Teachers, instructors,
and professors at the tertiary level must consider
"learning motivation," "perceived self-efficacy,"
and "students' interest" as essential variables in
encouraging students' academic progress.
Teachers and instructors are essential in fostering
the development of interests among students at
higher education level. Moreover, the
conceptualization of academic settings that
students confront is in the hands of teachers and
policymakers. The curriculum may be modified
and revised by these important stakeholders to
support the growth of each student's interest and
learning motivation.
Limitations of the study and
recommendations for further research
Everyone who participated in the present
investigation were undergraduate students, and
data were gathered from students at a single
public university. In this study, "learning
motivation," "perceived self-efficacy," and
"students' interest" were the only three factors
used to examine the connection and effect on the
CGPA score of undergraduate students. In the
future, studies could look into the relationship
between academic resilience, academic
commitment, burnout, and anxiety as a mediator
or moderator, as well as verify the impact on
students' CGPA at private and public universities
to obtain more generalizable results.
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