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