









































Amadiok, D., Abroampa, W.K., & Osei, E.O.  
Global Journal of  Transformative Education (2024) Vol 4   
pp 76-86  DOI  10.14434/gjte.v4i1.36683

Open Access

Published by the Global Insitutute of Transformative Education (http://www.gite.education)
© Amadiok, Abroampa, & Osei. 2024. Open Access This journal is distributed under the terms of the Creative Commons 
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Students’ Use Behaviors of E-Learning Management Systems in 
Ghanaian Public Universities: What do the demographics say?

Daniel Amadiok,1 Winston Kwame Abroampa,2 & Eric Opoku Osei3

Abstract
It is crucial to identify and offer intense training to groups of students who underutilize e-learning 

management systems. This study used a quantitative research methodology to evaluate demographic 
differences in students’ use of e-LMS in Ghanaian public universities. A questionnaire was used to collect 
data from 531 students. The techniques for data analysis included independent t-tests, mean computation, 
percentage estimation, and standard deviation estimation. Based on age and educational level, the results 
showed that there were no statistically significant variations in how students used e-LMSs. Nevertheless, 
a statistically significant variation in their use behavior with regard to gender and academic major was 
established. Particularly, social science majors and female students in Ghana’s public universities showed low 
use behavior of e-LMS. Therefore, it is recommended that students majoring in social sciences and those who 
are female undergo enhanced training on using the e-LMS platform. 

Keywords: Demographic characteristics, e-Learning management systems, Use behavior 

Full listing of authors and contacts can be 
found at the end of this article.

Introduction
It is impossible to overestimate the importance of 

technology in education, particularly in higher educa-
tion, where it has become an absolute necessity rather 
than an option. Technology integration has become 
crucial for every educational organization in the twen-
ty-first century (Alshehri, 2020). When compared to 
conventional classroom approaches, technology has a 
number of benefits for education that greatly speed up 
the teaching and learning processes (Aldowah, Ghazal, 
Umar, & Muniandy, 2017). With the use of digital tools, 
educators may more quickly and effectively impart 
knowledge, look for answers to students’ learning 
problems, and offer assistance. Technology also gives 
teachers the tools to interact with students who may be 
dispersed across various geographical regions. Addi-
tionally, technology helps students study more efficient-
ly and gives them the tools they need to take charge of 
their own education. Through digital tools, students 
have access to a wide range of educational resources, 
enabling them to customize their learning to meet their 
unique needs. Due to the crucial role that digital tools 

play in the administration and delivery of higher 
education, numerous educational institutions through-
out the world have made significant investments in 
digital technologies to assist teaching and learning. The 
e-learning management system is one such piece of 
technology that is essential to many academic endeav-
ors (Kasim & Khalid, 2016; Wichadee, 2015).

E-learning management systems are comprehen-
sive software solutions created for the creation, man-
agement, and dissemination of educational informa-
tion to students (Lang & Pirani, 2014). These systems 
are referred to by a variety of titles in the academic 
community, including virtual learning environments, 
course management systems, and learning content 
management systems (Ghilay, 2019). The varied names 
associated with these platforms naturally reflect their 
main objectives, which center on course management 
and learning facilitation. Numerous studies also offer 
various viewpoints on e-learning management systems. 
They are frequently portrayed as self-contained online 
learning platforms with storage capabilities that enable 
instructors to create and distribute educational content 
while also keeping track of student involvement (Tseng, 
2020). They also function as centralized hubs for uni-

76



Amadiok, Abroampa, Osei, Student Use of eLearning Mgmt Systems 77

versities, enabling academic institutions to efficiently 
plan and manage online teaching and learning (Nicho-
las-Omoregbe, Azeta, Chiazor, & Omoregbe, 2017). As 
Sharma, Gaur, Saddikuti, and Rastogi (2017) suggested, 
these systems can also distribute instructional infor-
mation through the Internet, enabling flexible lesson 
planning to accommodate students who are spread out 
geographically. E-learning management systems are 
web-based solutions that encourage teacher-student 
interaction and collaboration, foster the dissemination 
of knowledge, and strengthen the learning process. 
Colleges use these systems to perform both synchro-
nous and asynchronous e-learning activities, as well as 
hybridized deliveries, giving online education a flexible 
and all-encompassing approach.

The variety of e-LMSs used in higher education 
institutions is expanding because of the development of 
information and communication technologies. Because 
there are so many alternatives, different institutions 
choose particular systems (Xhaferi, Bahiti, & Imeri, 
2015). Notable e-learning management systems include 
Moodle, eCollege, Canvas, Sakai, and WebCT (An-
namalai, Ramayah, Kumar, & Osman, 2021; Matarirano, 
Jere, Sibanda, & Panicker, 2020). These e-LMSs provide 
a wide range of functionality, such as discussion fo-
rums, gradebooks, announcement tools, and file man-
agement capabilities, to help students and educators 
navigate virtual learning environments (Biney, 2020). 
Additionally, some of the applications might include 
extra functionality, including content creation, delivery, 
management, assessment, email, chat, list servers, in-
stant messaging, and discussion forums (Akay & Gumu-
soglu, 2020).

Through e-learning management systems, edu-
cators have access to a variety of big data like never 
before. To incorporate learning analytics into the teach-
ing and learning process, these systems automatically 
generate enormous amounts of data, which are subse-
quently examined using statistical tools (Ismail, Hamid, 
& Chiroma, 2019; Matsebula & Mnkandla, 2017; Lenar, 
Jamila, Ego, & Rustem, 2019). Predictive algorithms and 
forecasting tools that draw on data from educational in-
stitutions are used in e-learning management systems. 
Institutions can better understand students’ learning 
patterns and forecast their chances of success by care-
fully evaluating the data kept in log files within these 
systems (Yu & Jo, 2014). Importantly, in these learning 
environments, several elements, including study time, 
peer relationships, adherence to study schedules, and 
frequency of downloads, have a significant impact on

students’ academic development. Dashboards in 
e-learning management systems track and assess 
students’ academic progress over time, producing 
important metrics including completion rates, involve-
ment in the e-LMS, attendance data, and the likelihood 
of academic success. These indicators give educational 
authorities the ability to glean important insights into 
the growth and achievement of their students. Institu-
tions use the data collected from teacher and student 
actions inside these systems to improve and hone their 
pedagogical strategies.

The broad adoption and effective implementation 
of e-learning systems in educational institutions de-
pend on their acceptance by three crucial stakehold-
ers: educational institutions themselves, teachers, and 
students (Edumadze et al., 2014). However, it is crucial 
to acknowledge the notable discrepancies in students’, 
instructors’, and administrators’ readiness to use 
e-LMSs (Ansong, Boateng, & Boateng, 2016). The level 
of e-learning system use in higher education institu-
tions is determined by elements like the educational 
content’s quality and the accessibility of necessary 
resources like chat platforms, forums, and collabora-
tive features. Several studies (Ayouni, Menzli, Hajjej, 
Maddeh & Al-Otaibi, 2021; Kuadey et al., 2022; Mtebe, 
2015; Juhanak, Zounek & Rohlikova, 2019) have con-
firmed the widespread adoption of e-LMSs at institu-
tions of education and universities around the world. 
They also highlighted the growing adoption tendency in 
both well-resourced and under-resourced institutions. 
The high adoption of e-LMSs has failed to transfer into 
proportionate students’ use behavior of the platforms 
in sub-Saharan Africa (Dampson, 2021; Mtebe, 2015). 
While studies (Dampson, 2021; Tagoe & Cole, 2020; 
Asamoah, 2020; Sahoo, Odame, Reddy & Khan, 2020) 
acknowledge the general underutilization of e-LMSs in 
public universities in Ghana, there is a dearth of knowl-
edge and empirical findings on the influence of demo-
graphic factors on the overall non-use of e-LMSs by stu-
dents. Therefore, it is necessary to thoroughly examine 
various demographic groups to determine those con-
tributing to the underutilization of e-LMSs within Gha-
na’s public universities. In fact, analyzing student usage 
behavior based on demographic categories can give 
university administrators insightful information that 
will help them customize support programs to meet the 
specific requirements of these groups. Furthermore, a 
thorough comprehension of the variations in students’ 
use of e-LMSs has enormous promise for developing 
applicable regulations and enhancing training meth-

Global Journal of Transformative Education (2024) Vol 4



78

Global Journal of Transformative Education (2024) Vol 4

ods in higher education. Thus, the research questions 
this paper sought to answer are:
1. What are students’ use behaviors of e-learning man-

agement systems?
2.	 What	statistically	significant	differences	exist	in	

students’ use behaviors of e-learning management 
systems based on their demographic characteristics?
To add contextual knowledge to the research ques-

tions, the ensuing section presents a review of studies 
related to the themes extracted from the research 
questions.

Students’ use behavior of e-LMS
The words “use behavior,” “actual use,” or “actual 

usage” are widely used interchangeably in numerous 
studies in the field of information systems. These phras-
es jointly express the idea of a firm commitment to us-
ing a certain technology or information system (Black, 
1982). Students’ usage behavior of e-learning manage-
ment systems in the specific context of education serves 
as a visible indication of their persistent dedication to 
utilizing these educational platforms (Yakubu & Dasuki, 
2018). The importance of behavioral intentions and 
perceived behavioral control in determining the actual 
use of information systems is stressed by theoretical 
frameworks like the Technology Acceptance Model 
(Davis, 1986), the Theory of Planned Behavior (Azjen, 
1991), and the Unified Theory of Acceptance and Use 
of Technology (Vankesh, Thong, & Xu, 2016). These 
theories stress that people’s intentions and perceptions 
of their level of control over their behavior are the 
primary forces behind their actual interactions with 
technology. Assessment of students’ use behavior of 
the teaching and learning platform is a crucial compo-
nent of this study. Such an assessment not only clarifies 
the degree of their contact with the systems but also 
provides priceless information about the platform’s 
overall influence on their academic endeavors. As a 
result, a thorough examination of students’ actual use 
behavior serves as a vital benchmark for determining 
the usefulness and efficacy of the platform in improv-
ing the educational experience of students. Numerous 
studies have acknowledged students’ actual use behav-
iors of e-LMSs in advanced countries as extremely high. 
However, these same studies have pronounced that the 
application is underutilized in the sub-Saharan African 
context (Dampson, 2021; Tagoe & Cole, 2020; Asamoah, 
2020; Mtebe, 2015). Some groups of students are the 
cause of the underutilization and must be identified.

Gender segregation in e-LMS use
In several studies examining students’ uses of 

technology, the influence of gender as a demographic 
element has been examined. According to Cai, Fan, and 
Du’s (2017) research, the gender gap in technology use 
has only minimally closed, indicating that there is still a 
significant gap between males and females. In a similar 
vein, Mumporeze and Prieler’s (2017) study indicated 
that women utilize technology on average less than 
men do, and Qazi et al. (2021) discovered that men 
are more likely to use ICTs than women. Additionally, 
Alshorman and Bawaneh (2018) found that men tend 
to have more positive sentiments toward their use 
of technology than do women. Men utilize learning 
management systems more actively than women, 
according to Borokhovski, Tamim, Pickup, Rabah, and 
Obukhova (2019). In addition, Lim et al. (2020) used 
factorial invariance analysis to distinguish between 
how men and women use e-learning management 
systems. Dahlstrom & Bichsel (2014) reported that 
men and women use technology equally, which is 
in opposition to these findings. They asserted that 
inequalities in attitude are to blame for the disparities 
in technology use. Dahlstrom and Bichsel’s (2014) 
assertion is supported by the studies (Alshorman & 
Bawaneh, 2018; Yalman, Basaran, & Gonen, 2016). 
There are few studies that prove the distinctions 
between male and female students’ uses of e-learning 
management systems in Ghana’s public universities. 
Thus, this current study proposes the hypothesis:

H01:	There	is	no	statistically	significant	difference	in	
students’ use behaviors of e-learning management 
systems based on their gender.

Age segregation in e-LMS use
Numerous studies have shown that age can have 

a variety of effects on how people use technology. 
Researchers (Scherer, Siddiq, & Teo, 2015; John, 2015; 
Guillén-Gámez, Lugonesb, & Mayorga-Fernándezh, 
2019) have noted that age influences how people 
utilize technology. According to Cabero and Barroso’s 
(2016) research, younger males tend to excel at 
technology and utilize it more frequently than their 
older counterparts. This assertion was supported by a 
similar study by Gudmundsdottir and Hatlevik (2018), 
which emphasized that younger students have better 
technology usage abilities than older people. Guillén-
Gámez, Lugonesb, and Mayorga-Fernándezh (2019) 
backed up the conclusions made by the studies (Cabero 

Amadiok, Abroampa, Osei, Student Use of eLearning Mgmt Systems



79

& Barroso, 2016; Gudmundsdottir & Hatlevik, 2018) 
by emphasizing that younger students use technology 
more frequently than their older peers. Furthermore, 
Onyeaka, Romero, Healy, and Celano (2020) discovered 
that younger students engage with a larger variety of 
technologies than their older counterparts and have 
more expertise in using technology. In contrast, John 
(2015) found that those over the age of 30 had more 
favorable opinions about their usage of technology than 
people under that age. Despite these diverse findings, 
only a small number of studies have looked at how age 
affects how students use e-learning management sys-
tems. Particularly, there has not been much coverage of 
the higher education scene in Ghana. Consequently, to 
investigate this assertion, the following hypothesis has 
been formulated:

H02:	There	is	no	statistically	significant	difference	in	
students’ use behaviors of e-learning management 
systems based on their age.

Educational level segregation in e-LMS use
Students are normally divided into two groups in 

universities all around the world: undergraduates and 
postgraduates. There are differences between how 
these two groups of students use technology. Several 
studies have examined these student groups’ perspec-
tives on how they use e-learning management systems. 
For instance, postgraduate students exhibit a high level 
of proficiency when utilizing e-learning management 
systems, according to Buthelezi and Wyk’s (2020) 
research. Postgraduate students, on the other hand, 
exhibit little involvement with e-learning management 
systems (Kite et al., 2020). According to Dahlstrom 
and Bichsel’s (2014) research, undergraduates did not 
show a lot of enthusiasm for using e-learning manage-
ment systems in practice. In contrast to Sahoo, Odame, 
Reddy, and Khan’s (2020) finding that undergraduates 
are skilled at using these systems, Firat’s (2016) study 
emphasized distinct usage habits with regard to un-
dergraduates’ use of e-learning management systems. 
It is clear from reading these studies that we still don’t 
fully grasp the differences between undergraduates and 
postgraduates in terms of how they use e-learning man-
agement systems. To address this gap, the following 
hypothesis has been formulated:

H03:	There	is	no	statistically	significant	difference	in	
students’ use behavior of e-learning management 
systems based on their educational level.

Academic major segregation in e-LMS use
Academic majors are split into two groups in 

Binyamin’s (2019) taxonomy of university courses: 
science and social science. According to Binyamin, 
science students concentrate on fields like medicine, 
applied sciences (like computer science and engineer-
ing), and natural sciences (like biology, physics, and 
chemistry). In contrast, social science students focus 
on the humanities (such as history, religion, education, 
languages, and management). Ngah et al.’s (2022) study 
pointed out that there are variances in how students 
use technology. Students studying pure science out-
performed those studying social science in terms of 
technology use. This occurrence was explained by the 
fact that students studying pure science are engaged 
in practical activities and want to employ technology. 
Despite this categorization, a limited number of studies 
have explored the variations in technology usage based 
on these divisions. Consequently, this study aimed to 
investigate the disparities in students’ use behavior of 
e-LMSs based on their academic major. To examine this 
assertion, the hypothesis below has been formulated:

H04:	there	is	no	statistically	significant	difference	in	
students use behavior of e-learning management 
systems based on their academic major.

Methodology
All students enrolled in Ghanaian public universi-

ties who had used their institution’s e-Learning Man-
agement System (e-LMS) for at least a year comprised 
the study’s target population. The accessible population 
involved three public universities in Ghana because of 
their use of structures that enable students to collect 
data for academic purposes. However, Saunders, Lewis, 
and Thornhill (2019) advise using a multistage random 
sample strategy, which the researchers did because of 
the geographically scattered distribution of students 
within the universities. The faculties, along with their 
corresponding departments, made up the study’s nat-
ural clusters. The sampling procedure was conducted 
in stages, with the first stage involving the selection of 
one faculty (within a specific division in a college) from 
each of the three universities using the lottery method. 
The second stage involved choosing one department 
from each of the three faculties selected using the 
lottery technique once more, without replacement. In 
the final stage, student participants were selected using 
simple random sampling with the randomizer software 
as suggested by Creswell and Creswell (2018).

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Amadiok, Abroampa, Osei, Student Use of eLearning Mgmt Systems



80

The accessible population, which included 4,002 
students, consisted of those from the three universities 
that were selected during the initial sampling phase. 
Using a chart created by Krejcie and Morgan in 1970, 
a sample size of 825 was determined. After a thorough 
assessment of the literature, a paper-based question-
naire was created and given to the respondents. This 
decision was because, as Binyamin (2019) suggests, 
print versions have a higher response rate than online 
distribution. A five-point Likert-type scale was used to 
rate the adapted questionnaire items. Three ICT lectur-
ers reviewed the questionnaire items to make sure they 
were valid. The questionnaire’s pre-testing produced 
a reliability index for Cronbach’s alpha of 0.957. The 
respondents were given a total of 825 questionnaires; 
however, only 598 of these were filled out and returned. 
Sixty-seven questionnaires were declared unsuitable 
for further analysis after a preliminary evaluation be-
cause of missing values and suspicious patterns. Con-
sequently, 531 questionnaires, accounting for 64.4% of 
the total, were included in the final analysis. Research 
question 1, which was descriptive, involved looking 
at frequencies, percentages, averages, and standard 
deviations. Contrarily, an independent t-test was used 
to analyze research question 2, which was inferential in 
nature.

Data analysis
Demographic data 

The study elicited students’ gender, age, educational 
level, and academic major as demographic data. Table 1 
presents the demographic data of 
the respondents. 

Data in Table 1 show that 
males and females made up 
54.4% and 45.6%, respectively, of 
the total number of respondents. 
The mean age of the respondents 
was 23.82. Students below the 
mean age and students above the 
mean age were (302) 56.9% and 
(229) 43.1%, respectively, of the 
total respondents. Undergradu-
ates were 449 (84.6%), whereas 
postgraduates were 82 (15.4%). 
Students who had science as 
their academic major were 197 
(37.1%), whereas those with 
social science as their academic 
major were 334 (62.9%).

In summary, the analysis of Table 1 suggests that, 
firstly, there were more male respondents than female 
respondents. Secondly, the number of students below 
the mean age exceeded those above the mean age. 
Thirdly, undergraduates outnumbered postgraduates. 
Lastly, there were more respondents with a background 
in the social sciences compared to those with a pure 
science background in this study.

Students’ use behavior of e-LMS
Frequency, percentage, mean, and standard 

deviation were computed from the data on students’ 
use behavior of e-LMS. Table 2 depicts the results.

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Amadiok, Abroampa, Osei, Student Use of eLearning Mgmt Systems



81

The data in Table 2 show that students 
agree (M = 3.43; SD = 1.149) that they use their 
university’s e-LMS frequently. Again, the data 
in Table 2 show that students were unsure (M 
= 3.28; SD = 1.179) that they depend on the 
university’s e-LMS for their studies. Moreover, 
from the table, students agreed (M = 3.71; SD = 
1.043) that they tend to use their university’s 
e-LMS for as long as is necessary. Finally, the 
data in Table 2 show that students were unsure 
(M = 3.43; SD = 1.061) of the fact that they 
tend to use many features of their university’s 
e-LMS. 

Summarily, Table 2 shows that students 
were unsure (M = 3.46; SD = 1.11) of their use 
behavior of their university’s e-LMS. It is, thus, 
deduced that in public universities in Ghana, 
students are unsure of their usage behavior of 
e-LMSs.

Students’ use behavior of e-LMS in terms of 
their demographic characteristics

The results of the analysis of students’ use behavior 
of e-LMSs based on their demographic characteristics 
are presented in this section. The demographic 
characteristics are gender, age, academic level, and 
academic major. The results are shown in Table 3.

Table 3 reveals a statistically significant difference 
(p = 0.004;    = 0.05) between males and females in 
their use behavior of e-LMS. The table indicates that the 
mean of males (M = 3.567; SD = 0.9309) is higher than 
the mean of females (M = 3.336; SD = 0.9257). Thus, H01 
was rejected. It is, therefore, imperative that males have 
a higher use behavior of e-LMS than females.

Also, there was no statistically significant difference 
(p = 0.690;  = 0.05) between below-mean age and 
above-mean age in terms of their use behavior of 
e-LMS. Thus, H02 was accepted. A look at the mean 
scores in Table 3 shows that the mean score of students 
below the mean age (M = 3.476; SD = 0.9326) is similar 
to the mean score of students above the mean age (M 
= 3.443; SD = 0.9395). It can, thus, be deduced that 
students with their ages below the mean age have 
use behavior of e-LMS similar to that of students with 
their ages above the mean age in Ghanaian public 
universities.

Table 3 further shows that there was no statistically 
significant difference (p = 0.312;  = 0.05) between 
undergraduate and postgraduate students in terms of 
their e-LMS use behavior. Thus, H03 was accepted.

A look at the mean scores in Table 3 shows that the 
mean score of undergraduate students (M = 3.444; SD 
= 0.9431) is similar to the mean score of postgraduate 
students (M = 3.558; SD = 0.8874). Thus, it indicates 
that undergraduate and postgraduate students have 
similar use behaviors of e-LMS in Ghanaian public 
universities.

Finally, Table 3 indicates that there was a 
statistically significant difference (p = 0.01;  = 0.05) 
between students in the sciences and social sciences 
in terms of their use behavior of e-LMS, so H04 was 
rejected. Table 3 shows that the mean score of students 
with an academic major in science (M = 3.643; SD = 
0.942) is higher than the mean score of students with 
an academic major in social science (M = 3.355; SD = 
0.915). Thus, it implies that students in the sciences 
have a higher use behavior of e-LMSs than those in the 
social sciences in Ghanaian public universities.

Discussion
The study revealed that students were unsure 

(M = 3.46; SD = 1.11) of their use behaviors of their 
university’s e-LMS. This result corroborates the 
studies of Dampson (2021), Tagoe and Cole (2020), 
Asamoah (2020), and Mtebe (2015), which revealed 
that students underutilize e-LMSs in universities in the 
sub-Saharan context. These findings are attributable to 
a specific subset of students who either demonstrate a 
limited level of engagement with e-LMSs or, in some.

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Amadiok, Abroampa, Osei, Student Use of eLearning Mgmt Systems



82

cases, do not engage with them at all.
It also became known that there was a statistically 

significant difference (p = 0.004;  = 0.05) in students’ 
use behavior of e-LMSs based on their gender. This 
result aligns with studies that claim that there is a 
gender imbalance in the use of e-learning systems 
and that males use e-LMSs more frequently than 
females. The study by Li, Wang, and Campbell (2015) 
found that male students use e-LMS more regularly 
than females. Moreover, Alshorman and Bawaneh’s 
(2018) study found that males have higher attitudes 
toward technology than females. Contrary to this study, 
Alblassi’s (2016) study revealed that e-LMS use among 
males and females was the same. Likewise, Dahlstrom 
and Bichsel’s (2014) study found that males and 
females have the same abilities for utilizing information 
technologies. Moreover, the study by Cai, Fan, and Du 
(2017) found a reduction in the gap between men and 
women in terms of technology utilization. This finding 
of the study is in congruent with most studies regarding 
information technology utilization. The result indicates 
that female students in Ghanaian public universities 
need more training on e-LMSs to build optimal 
utilization behavior. This result might have occurred 
because males are more tech-savvy than females and 
would want to use more technology in their activities 
(Dahlstrom & Bichsel, 2014). 

The study also found that there was no statistically 
significant difference (p = 0.690;  = 0.05) in students’ 
use behavior of e-LMS based on their age. This result 
is inconsistent with most of the studies (Cabero & 
Barroso, 2016; Gudmundsdottir & Hatlevik, 2018; 
Guillén-Gámez, Lugonesb, & Mayorga-Fernándezh, 
2019) in this research area. These studies found that 
younger students tend to use e-LMSs more than older 
students. The findings of this current study seem to 
have occurred because the age differences among 
respondents was small. All ages in the 21st century 
have equal access to various kinds of technologies and 
skill development, which may be influencing their use 
behaviors of e-LMS, hence this result.

It was realized that there was no statistically 
significant difference (p = 0.312;  = 0.05) in students’ 
use behavior of e-LMS based on their educational 
level. This result indicates that undergraduates and 
postgraduates have the same use behavior for e-LMS. 
Several studies (Sahoo, Odame, Reddy, & Khan, 2020; 
Buthelezi & Wyk, 2020; Firat, 2016) in this research 
area have considered students use of e-LMSs based on 
either undergraduates or postgraduates. However, this

study compared both groups use of e-LMSs. The result 
might have occurred because both undergraduate and 
postgraduate students are subjected to e-LMS use in 
the same way.

The study further found that there was a 
statistically significant difference (p = 0.01;  = 0.05) 
in students’ use behavior of e-LMS based on their 
academic major. This finding indicates that students 
with an academic major in science have a higher use 
behavior of e-LMS than students with an academic 
major in social science. This corroborates the study 
of Ngah et al. (2022), which revealed that there was a 
statistically significant difference between pure science 
and social science students in terms of their usage of 
technologies. Pure science students utilize technologies 
that offer them more firsthand practical activities than 
social science students. This finding is not surprising in 
the Ghanaian higher education context because science 
students tend to be more practice-oriented and prefer 
to use technologies in their studies than social science 
students. Science students have specialized ICT tools 
for modeling, data analysis, and simulations in their 
course areas.

In summary, there was no difference in students’ 
use behaviors of e-LMSs in terms of age and educational 
level. However, there was a difference in students use 
behaviors of e-LMS based on their gender and academic 
majors. Female and social science students underutilize 
e-LMSs; therefore, enough training on e-LMS usage 
should be offered to them. The management of public 
universities in Ghana should give special attention to 
females and social science students when strategies 
and policies on e-LMS are being applied to them. It is 
imperative that the capacity of all students, irrespective 
of their background, be developed to enable them to 
conveniently use LMS.  

The potential limitation of this study is that it 
relied on a self-reported survey. However, respondents 
were informed of the ethical conditions under which 
the study was being conducted, to encourage genuine 
responses to the questionnaire. 

Conclusion
The purpose of this study was to examine the 

demographic differences in students’ e-LMS usage 
behavior. According to the study’s findings, neither 
students’ age nor educational level significantly 
affected how they used an e-LMS. However, there were 
noticeable differences in how students used the e-LMS 
based on their academic major and gender. As a result,

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we suggest that e-LMS usage regulations and proce-
dures be applied consistently to all students. However, 
special attention should be given to female students 
and students majoring in social science areas. It is 
significant to emphasize that the generalizability of 
this study is constrained to Ghana’s traditional public 
universities. Further research involving technical and 
private universities is advised in order to have a more 
thorough picture of e-LMS usage patterns across all uni-
versities in Ghana. This increased research effort would 
provide a more comprehensive viewpoint on e-LMS use 
behavior within the Ghanaian higher education sector.

Acknowledgement
Dr. Partrick Swanzy, the entire staff, and the entire 

student body of the Department of Teacher Education 
at Kwame Nkrumah University of Science and Technol-
ogy are all acknowledged and thanked for their encour-
agement, which has led to this paper.

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1 Daniel Amadiok (danieladmadiok@gmail.com) is a 
Doctoral Candidate in the Department of Teacher 
Education at Kwame Nkrumah University of Science and 
Technology, Kumasi, Ghana.

2 Winston Kwame Abroampa (wynxtin@yahoo.com) is 
an Associate Professor in the Department of Teacher 
Education at Kwame Nkrumah University of Science and 
Technology, Kumasi, Ghana

Authors

3 Eric Opoku Osei (eoosei@gmail.com) is a Senior 
Lecturer in the Department of Computer Science at 
Kwame Nkrumah University of Science and Technology, 
Kumasi, GhanaNetwork, Accra, Ghana.

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