Adoption of technology on E-learning effectiveness

Bulletin of Electrical Engineering and Informatics
Vol. 9, No. 3, June 2020, pp. 1121~1126
ISSN: 2302-9285, DOI: 10.11591/eei.v9i3.1717  1121
Journal homepage: http://beei.org
Adoption of technology on E-learning effectiveness
Zarina Denan, Zarina Abdul Munir, Rahayu A. Razak, Kardina Kamaruddin,
Veera Pandiyan Kaliani Sundram
Faculty of Business Management, Universiti Teknologi MARA Cawangan Selangor, Malaysia
Article Info ABSTRACT
Article history:
Received Aug 21, 2019
Revised Nov 14, 2019
Accepted Feb 28, 2020
The incorporation of E-learning in both private and public tertiary education
can help expedite the learning process. The utilization of fast-paced
technology with E-learning also allows for a more flexible and convenient
learning process. E-learning platforms can be accessed anywhere as long as
there is an internet connection, including at home, the workplace, restaurants
or while travelling. This allows for the benefit of distance learning. As such,
the current study aims to examine the factor effectiveness of E-learning
based on three variables, namely technology, instructors’ characteristics
and students’ characteristics and their impact on distance learning.
The education system has greatly evolved from the use of apparatus such as
chalk and blackboards to the modern use of projectors to conduct lessons.
In the current age, E-learning will have an effect on both instructors
and teaching technology, aside from the students themselves. As an example,
students are expected to know how to utilize these systems in their lessons,
instructors must receive training in E-learning systems management and in
terms of technology, the E-learning systems must be updated and operated
using the most recent upgrades. E-learning is also cost-efficient, less time
consuming and reduces theburden on both students and educators.
Keywords:
Instructors characteristic
Learning effectiveness
Students’characteristic
Technology
This is an open access article under the CC BY-SA license.
Corresponding Author:
Zarina Denan,
Faculty of Business Management,
Universiti Teknologi MARA Cawangan Selangor,
Kampus Puncak Alam, 42300 Bandar Puncak Alam, Selangor, Malaysia.
Email: ZarinaDenan2516@outlook.com
1. INTRODUCTION
The modern learning environment widely employs technology, specifically in the form of E-learning
systems.E-learning is an electronic module that functions through the use of the student’s computer, and thus
does not require the direct presence of an instructor or teacher. E-learning exists for the benefit
of internet-based education which can also be facilitated across distances. This form of learning requires
plenty of active interaction in order to attract and engage students. Such elements include examinations,
quizzes and other enrichment tasks. E-learning systems must be implemented and conducted within
the parameters of established guidelines, due to the fact that this programdoes not utilize a facilitator to carry
out the teaching process [1]. E-learning is an online educational system which allows for learning at any
place or time. Initial E-learning formats employed a combination of various PC-centric functions such as
compact disc programs. Modern E-learning however, is primarily conducted through the internet. E-learning
systems use electronic media to facilitate these guidelines or preparation phases. However, such instructions
only serve to highlight the accessibility of the resources as opposed to the expected outcomes or proven
results of the system [2]. E-learning functions as a form of communication between students and teachers
and a medium which requires mutual effort. It is also offered on an optional basis, with its foundation placed
on the education field and the communications industry. This is a tactic in order to evolve learning processes

 ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 9, No. 3, June 2020 : 1121 – 1126
1122
in the current global and modern context. The traditional “board and talk” method” of teaching where
the educator conducts lectures and offers handouts with minimal interaction and engagement fro m students
is no longer considered an effective learning or teaching processes.
The function of the E-learning module in adapting to current trends of knowledge acquisition
is a suitable and necessary evolution in educational methods. Typically, higher level education involving
college or university students will require improved and more productive methods to gain knowledge
and foster ideas. This modern approach to seeking knowledge will encourage students and provide themwith
the necessary confidence in their own potential and achievements. E-learning must not be conflated with
M-learning systems, which emphasize the utilization of internet-enabled devices such as tablets, laptops
or mobile phones. E-learning on the other hand, is specifically constructed for PCs, web communication
and the internet. The ability to exchange ideas, perspectives and data through the internet with multiple
parties in a flexible manner allows for more impactful communication [1]. As such, the current study
has the objective of assessing the students’characteristics,instructors’characteristics and the technology,and
their collective impact on the effectiveness of E-learning.
E-learning technology is accessible to students regardless of physical location or the presence
of an instructor. E-learning is a viable alternative to traditional teaching environments where the lecturer
and students occupy the same physical space. Research reveals that E-learning has an impact on students’
academic outcomes. This can be seen in good assessment or examination results, as well as a general positive
attitude from the students. This reflects the fact that good results and positive reviews are necessary in order
to encourage the intention to utilize E-learning. Studies indicate that opinions regarding E-learning
and intention are among the strongest factors which influence the actual usage of E-learning sustems.
Attitudes also significantly impact intention, but the actual utilization of E-learning is what carries the most
powerful effect on academic performance [3]. In the educational environment, students’ characteristics are
fostered through the encouragement gained in the learning process.Enhanced utilization of technology in this
process will develop competency and motivate students to take more initiative, and this can result in more
productivity. The compelling element of E-learning methods is the capacity for allowing students to acquire
basic skills and competency with electronic devices, and for them to continue to take the initiative to learn
more on their own. This provides a greater sense of fulfillment in learning. As such, educational institutions
are encouraged to offer greater access to the relevant facilities and offer training regarding the proper ways to
productively utilize this technology [4]. Aside fromstudents, such learning or training is also essential for the
instructors. In addition to the skillset they already possess, this will assist instructors in developing more
creative teaching methods as well as more effective student evaluation systems.
Some researchers are of the opinion that training and skills are sufficient to sustain the effectiveness
of E-learning. Students and lecturers with strong skills and training also make for excellent prospects in terms
of future human resources [1, 5]. In addition to cutting down on time spent, E-learning also simplifies
the materials and resources necessary for teaching. This is due to the fact that various programs and gadgets
already exist within the system and are accessible to participating students and instructors. Such resources
reduce the burden of work for lecturers and also make things more convenient for long-distance students.
These students can take it upon themselves to work independently with some guidance from instructors when
necessary [6-16]. The method of instruction and the demographics of students have undergone tremendous
change over time [7, 9]. The approach to education and the resources available have markedly evolved in
order to accommodate the varying needs, abilities and levels of accessibility of students and instructors.
A notable element among the various changes that have occurred in this field is the growing role played
by innovation in the context of training. Students utilizing information technology (IT) is also a reflection
of how crucial the role of the internet has become in educational environments [10-12, 15]. This can be used
as evidence for the outcome of the studied variables in this research [5]. Based on the discussion above, three
hypotheses were constructed as follows:
a. Hypothesis 1: Instructors’characteristic is statistically significant to e-learning effectiveness.
b. Hypothesis 2: Students’characteristic is statistically significant to e-learning effectiveness.
c. Hypothesis 3: Technology is statistically significant to e-learning effectiveness.
2. METHODOLOGY
A total of 181 undergraduate students in Human Resource Management were chosen from local
universities to participate in a survey. This study incorporates 28 items used to distinguish and assess
students’ characteristics, instructors’ characteristics, technology and E-learning effectiveness. These items
were adapted from [6] and [4]. The interval scale was employed as a measurement to assess responses to
the adapted questions and the degree to which the respondent agreed or disagreed with the statements given
as outlined in Table 1. These are indicated on a four-point scale whereby 1- Strongly Disagree, 2-Disagree,
3-Agree and 4–Strongly Agree. The data collected was subjected to more analysis through partial least square
(PLS) in order to distinguish the predictors of E-learning effectiveness. The results reveal that the distribution

Bulletin of Electr Eng & Inf ISSN: 2302-9285 
Adoption of technology on E-learning effectiveness (Zarina Denan)
1123
of gender was higher for females as compared to males. The number of female respondents encompass
70.0% (n=119) of total respondents while 30.0% (n=51) of respondents consist of males. As such, it can be
seen that the vast majority of respondents in this study were dominated by female students.Most respondents
are between the ages of 21 years old to 30 years old with the percentage of 68.2% (n=116). This is followed
by respondents aged 31 years old to 40 years old with the percentage of 28.8% (n=49). While those aged
above 40 years old constitute 2.9% (n=5) of all respondents. Most of the respondents utilize the computer for
educational purposes for more than 5 hours, with the percentage being 43.5% (n=74). This is then followed
by those who use the computer for learning for 2-4 hours with the percentage of 32.9%% (n=56), while usage
between 1-2 Hours is 20.6% (n=35). Those who use the computer for educational purposes for under 1 hour
is 2.9 % (n=5).
The reason for utilizing PLS in this research is in order to identify the causal structure proposed
and to verify the hypothesis to the point where the respondents’ data supports the established structure.
The students’ characteristics, instructors’ characteristics and technology are all modeled as reflective
constructs due to the fact that these variables are interchangeable, unidimensional and reliable to an identical
degree. In other words, when one of the variables is removed from the model, the construct
is not altered [7, 17]. In PLS, there exist measurements and structural models to evaluate the validity
and reliability of constructs, as well as methods to test the hypothesis within the models. A measurement
model evaluates construct measurement and the process of validation by examining composite reliability
(CR), average variance extracted (AVE) and discriminant validity. The combination of the results of these
assessments create the measurement model. A set range of values must be acquired in order to establish high
reliability and zero error in terms of discriminant issues. In terms of composite reliability, the threshold value
must be higher than 0.5. The [8] analysis is utilized to assess discriminant validity, whereby the square root
of AVE for every construct must be greater than its correlations with other constructs.
3. RESULTS AND DISCUSSION
Table 1 shows the convergent validity values which are made up of factor loadings of items,
composite reliability and average variance extracted of E-learning effectiveness (CR=0.912, AVE=0.597),
instructors’ characteristics (CR=0.931, AVE=0.662), students’ characteristics (CR=0.875, AVE=0.517)
and technology (CR=0.899, AVE=0.563).
Table 1. Convergent validity
Variables Factor loading CR AVE
E-Learningeffectiveness
E1: E-Learningwill improve qualityofeducation.
E2: The E-Learningapproachis better thanthe traditional approach.
E3: The E-Learningapproachis moreenjoyable than thetraditional approach.
E4: E-Learningdoes not offer me any advantages.
E5: Communicationwith the instructorin the E-Learningenvironment was better thanthe
traditional environment
E6: The E-Learninginterfacetobe flexible tointeract with learning.
E7: My interaction withthe E-Learninginterface was clear andunderstandable.
0.717
0.749
0.857
0.764
0.806
0.692
0.808
0.912 0.597
Instructors characteristics
IC1: Instructors are friendlyandapproachable.
IC2: Instructors are easily contacted.
IC3: Instructors explainhowtouse the website at thebeginningof the semester.
IC4: Instructors encourage student interactions
IC5: Instructors provide sufficient learningresources online.
IC6: Instructors solveemergingproblem efficiently.
IC7: Instructors provide fast feedback to queries in the discussion forum.
0.785
0.871
0.589
0.804
0.887
0.876
0.846
0.931 0.662
Student characteristic
SC1: I am anxious in completingmy degree.
SC2: I have belief in my capabilitytointeract with technology.
SC3: I am cognitivelyengagedin doingthe E–Learningactivities.
SC4: I have the initiative and motivation to learn anduse the system.
SC5: I have high level of self – confidence in usingthe system.
SC6: I am willing to participate in E – Learningactivities.
SC7: I am satisfiedwith time andplace flexibility ofthe system.
0.785
0.871
0.589
0.804
0.887
0.876
0.846
0.875 0.517
Technology
T1: The systemallows easy access toinformation.
T2: Thereis informationcredibility in thesystem.
T3: The guidance screenis clear andeasy touse.
T4: The IT infrastructure is reliable andsecure.
T5: Thereis adequate investment in infrastructuretosupport electronic performance.
T6: I am rarelydisconnectedduringonline tutorial
T7: I am satisfiedwith thebrowsingspeed.
0.824
0.787
0.854
0.798
0.721
0.552
0.671
0.899 0.563

 ISSN: 2302-9285
1124
The results indicate that every item and construct within the model meet the threshold values
of CR which are greater than 0.7 and with AVE greater than 0.5. The factor loading of the items tested
were greater than 0.5 and suitable for use in the analysis. In terms of the measurement model, Table 2 depicts
the outcome of [18-20] analysis and establishes that the discriminant is reached. As such it can be concluded
that the main construct measures different aspects. Figure 1 shows the measurement model. As can be seen in
this figure, the student characteristics, instruction characteristics and technology are the main parameters
which affected e-learning effectiveness.
Table 2. Lerning results
Constructs 1 2 3 4
1. E-learningeffectiveness 0.772
2. Instructors characteristics 0.55 0.814
3.Student characteristic 0.753 0.499 0.719
4.Technology 0.707 0.62 0.606 0.75
Figure 1. Measurement model
3.1. Structural Model
The structural model was assessed based on the significance of the structural path coefficient,
the R square values (R2) [21]. To test the significance, [22] suggest that the bootstrapping of 500 resamples
be utilized to generate standard error (SE), t-statistic (t-values) and the percentile of 95 percent confidence
interval. The relationship between variables are indicated in Table 3.
Table 3. Relationship between variables
Relationship Beta STD T-Values P Values
LL UL
Result
2.50% 97.50%
Instructors characteristics E-learning
effectiveness
0.082 0.054 1.512 0.131 -0.018 0.186
H1: Not
supported
Student characteristic E-learning
effectiveness
0.497 0.06 8.342 0 0.375 0.594 H2: Supported
Technology E-learningeffectiveness 0.356 0.082 4.323 0 0.172 0.502 H3: Supported

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Adoption of technology on E-learning effectiveness (Zarina Denan)
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The results in Table 3 indicate that the constructs achieve higher values of R2 with 0.671 than
the substantial level indicated [23-25]. The two direct relationships which are students’ characteristics
(β=0.497, t-values=8.342, p<0.05) and technology (β=0.356, t-values=4.323, p<0.05) included in Figure 1
are statistically significant and accepted for instructors’ characteristics (β=0.082, t-values=1.512, p>0.05)
As such, Hypothesis 2 and 3 were supported in this study. The significance estimated are obtained using
a percentile bootstrap [26]. Results show that the lower limit and upper limit values do not contain zero,
and explained how the direct effects are significantly different from zero with 95 percent confidence [23].
4. CONCLUSION
In the current study, three independent variables, namely Students’ Characteristics, Instructors’
Characteristics and Technology, were comparatively assessed against E-learning effectiveness. The collected
data was analyzed using PLS statistics and the findings revealed that students’characteristics and technology
influenced E-learning effectiveness, in contrast to the lack of support from the variable of instructors’
characteristics. This study highlights the elements of attitude, motivation and behavior in the context
of the role played by students and in the effective and productive use of E-learning.
ACKNOWLEDGEMENT
This research was supported by BESTARI Grant of Universiti Teknologi MARA, Malaysia
(BESTARI 600-IRMI/SSP/DANA 5/3/BESTARI (00025/2016).
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