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Paper—Factors Affecting Students' Perspectives on the Usefulness of Learning Online
Factors Affecting Students' Perspectives on the
Usefulness of Learning Online
https://doi.org/10.3991/ijet.v17i15.33243
Tuong Cao Dinh(), Sang Minh Vo
FPT university, Can Tho, Vietnam
tuongdc@fe.edu.vn
Abstract—The more students perceive the usefulness of the learning mode,
the more they accept it, hence inference to their learning success. The purpose of
the study is to identify factors that impact students’ perspectives on the usefulness
of learning online in their learning process based on the Technology Acceptance
Model. Learners’ styles and their e-learning self-efficacy were integrated into the
model to explore the relationships between these factors and their perceived use-
fulness of learning online. A questionnaire survey was administered to 356 vol-
untary students of a private university in Vietnam. Data analysis methods include
the Cronbach’s alpha test to examine the scales’ reliability, Confirmatory Factor
Analysis method to determine the factors of learners’ learning online styles, and
the structural equation modeling method to estimate the correlations between the
dependent and independent constructs. The results indicate that four in six learn-
ers’ learning online styles had a significant relationship with perceived useful-
ness. In particular, tactile and group factors, and the Individual factor have di-
rectly and indirectly positive effects on perceived usefulness respectively while
Kinaesthetic factor has a direct negative effect on perceived usefulness. The other
factors such as visual and auditory show no relationship with perceived useful-
ness. Learners’ e-learning self-efficacy and perceived ease of use components
have a direct and positive impact on perceived usefulness. Pedagogical implica-
tions and limitations of the study are also discussed.
Keywords—TAM, learning styles, learners’ e-learning self-efficacy, online
learning, higher education
1 Introduction
Albeit some reported drawbacks on its limitations to learners’ interaction, online
learning has become ubiquitous in education at multi-levels, from primary to tertiary
education due to its flexibility, accessibility, and learners’ learning individualization
[1]. Among many well-known Massive Open Online Course (MOOC) platforms for
online courses, such as edX, Udacity, Khan Academy, and so forth, Coursera has con-
firmed its increase in growth worldwide [2]. The utilization of online learning alongside
traditional courses poses a need to identify learners’ readiness to learn in this new learn-
ing environment. Tsai and Lin’s [3] study showed that learners’ perceptions of the In-
ternet can affect their attitudes and behavior towards their Internet self-efficacy and the
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Paper—Factors Affecting Students' Perspectives on the Usefulness of Learning Online
usefulness of the Internet in their online learning. Developed from the Theory of Rea-
soned Action [4],[5], the Technology Acceptance Model (TAM) devised by Davis [6]
was widely used as a tool to predict users’ acceptance of new technology. In this model,
perceived usefulness (PU) and perceived ease of use (PE) are the two key determinants
that directly affect users’ behavioral intention to use the technology system. What is
more, this model allows the addition of external factors to verify other determinants of
the validity of the TAM constructs. External variables include system characteristics,
such as system functionality, system interactivity, system response time [7], [8], and
user characteristics comprised of self-efficacy, Internet experience, interaction, and
learners’ learning styles [1], [9].
Recently many scholars have utilized this theoretical framework to anticipate stu-
dents’ intention to use Virtual Reality in the classrooms [10], or in online learning en-
vironments [11], students’ continuance intention of learning in MOOC platforms [12],
in LMS [13], factors affecting students’ acceptance of technology as an instrument for
learning [14], factors affecting faculty’s intention to adopt online technological tools
[15], or to use MOOCs as a resource to attain their educational aims [16]. In Vietnam,
an increasing number of studies have employed the TAM to explore factors influencing
students as well as lectures’ adoption of technology and their attitudes and intentions
to use technology during the teaching and learning process in both online and blended
learning environments. For example, some studies focused on identifying factors af-
fecting students’ e-learning acceptance and students’ learning achievements [17],
teachers’ adoption of an online learning management system [18], and students’ learn-
ing online in public and private universities [19]. Others investigated students’ attitudes
toward the use of a blended learning environment [20], and students’ attitudes and in-
tentions toward the use of social media [21].
It can be drawn that although the adoption of TAM has been well documented, there
is a dearth of research examining students’ factors such as learning styles and their
perceived self-efficacy on their perspectives of the usefulness of online learning. This
gap gives an impetus for the current study, especially at the saddening outbreak of
COVID-19 when almost all tertiary educational institutions nationwide have no choice
but to have their students study online. In particular, this study adapted Al-Azawei et
al.'s [22] model in the context of e-learning in a private university in the Mekong Delta,
Vietnam, by examining the effects of the two aforementioned external variables on stu-
dents’ perspectives on the usefulness of online learning.
2 Research model and theoretical framework
2.1 An extension of the technology acceptance model (TAM)
Al-Azawei et al. [22] investigated the influence of learning styles based on the Index
of Learning Styles (ILS) Questionnaire by Felder and Soloman (n.d.) on students' sat-
isfaction in a blended learning environment. Their model included e-learning self-effi-
cacy (LE), learning styles (LS), perceived satisfaction, perceived usefulness (PU), per-
ceived ease of use (PEOU), and intention to use. Using PLS-SEM path modeling, they
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discovered that PEOU showed a direct significant effect on PU, LE had positive im-
pacts on PEOU and PU, whereas the hypothesis that LS had a positive direct influence
on PU was not supported. However, a study by Lee et al. [23] using Reid’s Perceptual
Learning Style Preference Questionnaire (PLSPQ) [24] indicated that four learning
styles, namely visual, auditory, kinesthetic, and tactile, were positively correlated with
their learning via network-based computer technology. The controversial roles of LS
during students' learning process in online learning have paved the way for further re-
search on this issue.
2.2 Learning styles (LS) and learners’ e-learning self-efficacy (LE)
Learning styles as an important predictor of students' research self-efficacy was con-
firmed by previous studies [25], [26]. In their study, students with more active and in-
tuitive LS showed higher self-efficacy in research. Similarly, using the Index of Learn-
ing Styles [27] as the instrument to assess students' LS, Direito et al. [28] found a high
relationship between undergraduate engineering students' LS and their self-efficacy in
their soft skills. Nevertheless, the correlation between these two factors varied [29], and
had no effect on each other [30]. In this paper, learning style factors include Visual
(Vis), Auditory (Aud), Kinaesthetic (Kin), Tactile (Tac), Individuals (Ind), and Group
(Gro). From prior research contradictory results, we propose that:
─ H1.1.1: Visual directly affects E-learning Self-efficacy (LE).
─ H1.2.1: Auditory directly affects E-learning Self-efficacy (LE).
─ H1.3.1: Kinaesthetic directly affects E-learning Self-efficacy (LE).
─ H1.4.1: Tactile directly affects E-learning Self-efficacy (LE).
─ H1.5.1: Individuals directly affect E-learning Self-efficacy (LE).
─ H1.6.1: Group directly affects E-learning Self-efficacy (LE).
2.3 Learning styles and perceived ease of use
A study conducted by Gu et al. [31] employing Structural Equation Modelling
(SEM) to examine the effects of students' learning styles on their Perceived Ease of Use
when studying online shows that students find it easy to use the online learning tool. In
a similar vein, findings from a recent empirical study by Lu, Lin, and Chen [32] sur-
veying 322 university students resonate with this result. However, Al-Azawei and
Lundqvist [33] did not find any direct significant influence of students' LS on PE.
Therefore, we hypothesize that:
─ H1.1.2: Visual directly affects perceived ease of use (PE).
─ H1.2.2: Auditory directly affects perceived ease of use (PE).
─ H1.3.2: Kinaesthetic directly affects perceived ease of use (PE).
─ H1.4.2: Tactile directly affects perceived ease of use (PE).
─ H1.5.2: Individuals directly affect perceived ease of use (PE).
─ H1.6.2: Group directly affects perceived ease of use (PE).
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2.4 Learning styles and perceived usefulness
This study employed Reid’s [24] Perceptual Learning Style Preference Question-
naire (PLSPQ) since it has been widely adopted in the past few decades to investigate
learners from different contexts and nationalities [23]. The questionnaire incorporates
30 purposively random items for the six learning style preferences, namely visual, au-
ditory, kinaesthetic, tactile, group learning, and individual learning. Participants in the
survey reply on a five-point Likert scale, ranging from strongly agree to strongly disa-
gree. Identifying learners’ learning styles would not only benefit learners themselves
but also various stakeholders. For students, it helps to enhance their learning perfor-
mances since learning in their favorable style would make their learning become more
enjoyable [34]. For instructors, comprehending students’ learning styles enables them
to choose appropriate educational activities and teaching materials to effectively boost
their students’ learning [35], [36]. In addition, Felder and Brent [35] suggested that the
insufficient balance of learners’ learning styles may lead to a dropout rate and poor
performance. However, pedagogical implications based on learning styles have been
still controversial due to the lack of convincing evidence to support it [33], [37]. It can
be inferred from the aforementioned studies that there seems to be a positive correlation
between students’ learning style preferences and learning environment as learners may
not be in favor of a learning environment if they do not perceive the usefulness of the
learning environment to their learning process. Therefore, the following hypothesis is
proposed:
─ H1.1.3: Visual directly affects Perceived Usefulness (PU).
─ H1.2.3: Auditory directly affects Perceived Usefulness (PU).
─ H1.3.3: Kinaesthetic directly affects Perceived Usefulness (PU).
─ H1.4.3: Tactile directly affects Perceived Usefulness (PU).
─ H1.5.3: Individual directly affects Perceived Usefulness (PU).
─ H1.6.3: Group directly affects Perceived Usefulness (PU).
2.5 Learner's e-learning self-efficacy with perceived ease of use and perceived
usefulness
Computer self-efficacy is defined as the user’s judgment of their capability to per-
form certain learning tasks when learning in an e-learning environment [7]. In this
study, e-learning self-efficacy refers to students’ beliefs about their ability to study in
online courses based on web-based instructions. Previous studies have indicated that
computer self-efficacy has a positive effect on Perceived ease of use [7], [38]-[40], and
on perceived usefulness as well [9], [22], [41], while this impact was challenged [42].
Hence, it is proposed that:
─ H2.1: Learners’ e-learning self-efficacy directly affects perceived ease of use.
─ H2.2: Learners’ e-learning self-efficacy directly affects perceived usefulness.
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2.6 Technology acceptance model
The technology acceptance model-TAM [6] based on the theory of reasoned action
by Fishbein and Ajzen [5] was proposed to predict users’ willingness to accept new
technology. The key variables in this model are the perceived usefulness (PU) and the
perceived ease of use (PE) which influence user behavioral intentions on using tech-
nology. Perceived ease of use was defined as “the degree to which an individual be-
lieves that using a particular system would be free of physical and mental effort." [43,
p.26], while perceived usefulness referred to the degree to which the user believes in
new technology that can boost his/her job performance (ibid.). Davis [43] hypothesized
that PE had a significant direct impact on PU. Prior research also indicated this direct
relationship [9], [42]-[46]. Hence, it is hypothesized as follows:
─ H3: Perceived ease of use (PE) directly affects perceived usefulness (PU).
In the present study, PE refers to the extent to which students find it easy to study in
Coursera and/or FUNiX courses, and PU refers to the degree to which students perceive
how effective their learning in E-learning (Coursera and or FUNiX platforms) is. The
current study model incorporates learning styles (LS), perceived e-learning self-effi-
cacy (LE), perceived ease of use (PE), and perceived usefulness (PU). Based on the
aforementioned hypotheses, our research model is presented in Figure 1.
Fig. 1. The proposed research model based on TAM model by Davis (1989)
3 Method
3.1 Participants
The present study sought to obtain the confidence level and the margin of error of
95% and 5% respectively. The population of the present study was 2043 students, so
the sample size was 343 participants [47]. Among 453 responses from the online
Google Form questionnaire, collected from June 08 to June 23, 2021, 356 were quali-
fied for data analysis after filtering data errors and/or duplicated ones. They are students
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aging from 18 to 20 majoring in Business, IT, and English at a private university in
Vietnam. They were selected as participants for the survey because they had taken at
least one subject on Coursera/FUNiX as required by their majors, and so fit the study
aim. Table 1 provides the demographics of the participants of the study.
Table 1. Participant demographics
N
Percentage (%)
Gender
Male
165
46.3
Female
191
53.7
Age
18-20
103
28.9
> 20
253
71.1
School year
1st year
34
9.6
2nd year
111
31.2
3rd year
114
32
4th year
97
27.2
Majors
Business
169
47.5
IT
145
40.7
English
42
11.8
3.2 Research instruments
The questionnaire survey employed in this study was adapted from prior studies. In
particular, the LS scale was adapted from Reid [24], and e-learning self-efficacy was
measured by Al-Azawei et al. [22]. In order to make sure that the respondents would
comprehend the items correctly, we translated them into Vietnamese which was care-
fully double-checked for language equivalents by two colleagues who both had a Ph.D.
and a Master’s degrees overseas, and then conducted a piloting phase before delivering
the questionnaire to the participants. Participants were required to rate each statement
according to a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly
agree).
3.3 Data collection and analysis
Prior to data collection, the questions were double-checked for language equivalents
by two colleagues who had a Ph.D. and a Master’s degrees overseas; the questionnaire
was then administered to 30 students for the piloting phase before being delivered to
the participants. Participants were required to rate each statement according to a 5-point
Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). The questionnaire
was sent to the participants via Google Forms. To ensure the consistency of every scale,
the Statistical Package for the Social Sciences (IBM SPSS) Statistics version 25 was
utilized for data analysis.
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4 Results
4.1 The reliability of the research instrument
Cronbach Alpha was run to test the internal consistency of every scale. Table 2
shows that all values are between 0.79 to 0.93 and the correlation coefficient of each
observed variable with the total variable is greater than 0.3, indicating the high reliabil-
ity of each construct.
Table 2. The results of Cronbach’s alpha test
Variables Code
Corrected Item-Total
Correlation
Auditory-Aud, Cronbach's alpha coefficient= 0.83
When the teacher tells me the instructions, I understand better.
Aud1
0.54
When someone tells me how to do something in class, I learn it better.
Aud2
0.67
I remember things I have heard in class better than things I have read.
Aud3
0.61
I learn better in class when the teacher gives a lecture.
Aud4
0.68
I learn better in class when I listen to someone.
Aud5
0.63
Group-Gro, Cronbach's alpha coefficient= 0.87
I get more work done when I work with others.
Gro1
0.67
I learn more when I study with a group.
Gro2
0.74
In class, I learn best when I work with others.
Gro3
0.70
I enjoy working on an assignment with two or three classmates.
Gro4
0.71
I prefer to study with others.
Gro5
0.68
Individuals-Ind, Cronbach's alpha coefficient= 0.88
When I study alone, I remember things better.
Ind1
0.61
When I work alone, I learn better.
Ind2
0.71
In class, I work better when I work alone.
Ind3
0.79
I prefer working on projects by myself.
Ind4
0.69
I prefer to work by myself.
Ind5
0.78
Kinesthetic-Kin, Cronbach's alpha coefficient= 0.84
I prefer to learn by doing something in class.
Kin1
0.62
When I do things in class, I learn better.
Kin2
0.73
I enjoy learning in class by doing experiments.
Kin3
0.70
I understand things better in class when I participate in role-playing.
Kin4
0.50
I learn best in class when I can participate in related activities.
Kin5
0.71
Tactile-Tac, Cronbach's alpha coefficient= 0.85
I learn more when I can make a model of something.
Tac1
0.63
I learn more when I make something for a class project.
Tac2
0.68
I learn better when I make drawings as I study.
Tac3
0.62
When I build something, I remember what I have learned better.
Tac4
0.66
I enjoy making something for a class project.
Tac5
0.71
Visual-Vis, Cronbach's alpha coefficient= 0.79
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I learn better by reading what the teacher writes on the chalkboard.
Vis1
0.44
When I read instructions, I remember them better.
Vis2
0.58
I understand better when I read instructions.
Vis3
0.67
I learn better by reading than by listening to someone.
Vis4
0.61
I learn more by reading textbooks than by listening to lectures.
Vis5
0.53
Learner’s e-learning self-efficacy-LE, Cronbach's alpha coefficient= 0.89
I can use e-learning (Coursera and/or FUNiX), if there is no one around to tell
me what to do as I go.
LE1 0.81
I can use e-learning (Coursera and/or FUNiX), even if I have never
used a system like it before.
LE2 0.73
I can use e-learning (Coursera and/or FUNiX), even if there are no as-
sistant illustration tools with the system.
LE3 0.80
Perceived ease of use-PE, Cronbach's alpha coefficient= 0.90
The interaction feature in e-learning (Coursera and/or FUNiX) is clear
and understandable.
PE1 0.76
Interacting with e-learning (Coursera and/or FUNiX) does not require
a lot of mental effort.
PE2 0.74
I would find it easy to get e-learning (Coursera and/or FUNiX) to do
what I want it to do.
PE3 0.82
I would find the e-learning (Coursera and/or FUNiX) easy to use.
PE4
0.80
Perceived usefulness-PU, Cronbach's alpha coefficient= 0.93
Using e-learning (Coursera and/or FUNiX) improves my performance.
PU1
0.87
Using e-learning (Coursera and/or FUNiX) increases my scientific per-
formance.
PU2 0.84
Using e-learning (Coursera and/or FUNiX) enhances my learning ef-
fectiveness.
PU3 0.87
4.2 Correlations between components of learning styles
Confirmatory factor analysis (CFA) was performed to analyze the reliabilities of 06
variables of LS, namely auditory (Aud), visual (Vis), group (Gro), tactile (Tac), kin-
aesthetic (Kin), and individuals (Ind). The result indicates that variables Aud and Vis
were eliminated due to their standardized loading estimates falling below 0.5 (stand-
ardized loading estimates of auditory and visual =0.49 and 0.37<0.5, respectively), ac-
cording to Hair et al. [48]. Therefore, the revised CFA analysis was as follows.
Table 3 shows that Chi-square/df = 2.7 < 3.0, TLI = 0.92, CFI = 0.93 were all larger
than 0.90, and RMSEA = 0.070 < 0.08. This means that the proposed model was ap-
propriate for further analysis of the surveyed data (Chin & Todd, 1995; Segar & Grover,
1993). Table 3 also indicates that three variables, namely group (Gro), tactile (Tac),
Kinaesthetic (Kin) and individuals (Ind), have standardized loading estimates higher
than 0.5 and statistically significant (P<0.01), responding to confidence level of 0.10,
indicating their good convergent validity [49].
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Table 3. Standardized loading estimates
Relationship
Estimate
Significance
Relationship
Estimate
Significance
Gro1
→
Gro
0.75
***
Kin1
→
Kin
0.64
***
Gro2
→
Gro
0.81
***
Kin2
→
Kin
0.77
***
Gro3
→
Gro
0.76
Kin3
→
Kin
0.78
Gro4
→
Gro
0.74
***
Kin4
→
Kin
0.58
***
Gro5
→
Gro
0.75
***
Kin5
→
Kin
0.83
***
Ind1
→
Ind
0.71
***
Tac1
→
Tac
0.68
***
Ind2
→
Ind
0.78
***
Tac2
→
Tac
0.75
***
Ind3
→
Ind
0.87
Tac3
→
Tac
0.69
Ind4
→
Ind
0.68
***
Tac4
→
Tac
0.74
***
Ind5
→
Ind
0.79
***
Tac5
→
Tac
0.80
***
Note. ***: p < 0.01
Table 4 indicates that the correlations between constructs in the CFA model had a
good discriminant validity, except for the correlation between Kin and Tac due to their
p-values (P=0.15) larger than 0.05 ([48], [51]).
Table 4. Correlations between subscales of LS
Correlation
Estimate
SE
CR
P
Kin
<-->
Tac
0.97
0.02
1.44
0.15
Kin
<-->
Ind
0.49
0.07
6.81
0.00
Kin
<-->
Gro
0.76
0.06
4.36
0.00
Tac
<-->
Ind
0.55
0.07
6.32
0.00
Tac
<-->
Gro
0.72
0.06
4.75
0.00
Ind
<-->
Gro
0.17
0.08
9.78
0.00
Table 5 indicates that all variables ensure the measurement reliability (Cronbach's
alpha > 0.8) and convergent validity of the model. All standardized loading estimates
in the CFA model are above the acceptable threshold of 0.5 and statistically significant
(p < 0.05). In addition, CR and AVE of all constructs are above the minimum acceptable
threshold 0.7 and 0.5 respectively ([48]; These, together, ensure the measurement reli-
ability and convergent validity of the model.
Table 5. Convergent and discriminant validity testing
Constructs Cronbach's alpha
Composite Reliability
(CR)
Average Variance Ex-
tracted (AVE)
Kinaesthetic
0.84
0.85
0.53
Tactile
0.85
0.85
0.53
Group
0.87
0.87
0.58
Individual
0.88
0.88
0.59
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In sum, Tables 3-5 indicate that the four variables of LS were ensured for reliability,
convergent validity, and discriminant validity of the CFA measurement model. In ad-
dition, the results of the aforementioned CFA analysis have confirmed the appropriate-
ness of the four components of LS in learning online in higher education, including:
Kinaesthetic, tactile, group, and individual. Two components, namely auditory and vis-
ual, were omitted. Therefore, Hypotheses H1.1.1-3 and H1.2.1-3 were eliminated.
4.3 Structural model
The structural equation modeling (SEM) method was performed to estimate the cor-
relations between the dependent variables, including Kin, Ta., Gro, Ind, and independ-
ent constructs, namely: learner’s e-learning self-efficacy (LE), perceived ease of use
(PE), and perceived usefulness (PU). The analysis results from SEM recorded: Chi-
square/df = 2.6 < 3.0; TLI = 0.91, CFI = 0.92 are both greater 0.90; RMSEA = 0.066 <
0.08: shows the results of multiple fit indices of the structural model. The result from
the initial SEM analysis indicated the correlations between these variables, as follows:
Table 6. The initial SEM analysis
Relationship
Λ
P
Gro
→
LE
0.64
***
Gro
→
PE
-0.10
0.26
Gro
→
PU
0.02
0.84
Ind
→
LE
0.43
***
Ind
→
PE
-0.13
0.07
Ind
→
PU
-0.14
0.17
Kin
→
LE
0.51
0.32
Kin
→
PE
-0.34
0.34
Kin
→
PU
-0.74
0.12
Tac
→
LE
-0.83
0.11
Tac
→
PE
0.55
0.14
Tac
→
PU
0.86
0.10
LE
→
PE
0.94
***
LE
→
PU
1.18
***
PE
→
PU
-0.24
0.22
The structural equation modeling (SEM) method was re-performed to eliminate the
non-significant correlations between the dependent and the independent constructs, as
indicated in Figure 1. The result indicated that the structural model fits the data sur-
veyed well (see Table 7 for further detail).
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Table 7. The final SEM analysis
Relationship
Λ
P
Gro
→
LE
0.57***
***
Gro
→
PU
0.20***
0.01
Ind
→
LE
0.35***
***
Kin
→
LE
-0.25**
0.03
Kin
→
PU
-0.58**
0.05
Tac
→
PE
0.14***
***
Tac
→
PU
0.54**
0.05
LE
→
PE
0.81***
***
LE
→
PU
0.65***
***
PE
→
PU
0.18**
0.03
***: P < 0.01; **: P < 0.05.
4.4 Hypotheses testing
From the results of Tables 6 and 7, we concluded that:
The path coefficient of kinaesthetic (Kin) to perceived ease of use (Kin→PE) is not
significant at 0.05 level (λ=-0.47, P=0.34>0.05), hence H1.3.2 is rejected. Furthermore,
Kin has direct negative effects on learners’ e-learning self-efficacy (LE) (λ=-0.25,
P=0.03<0.05) and perceived usefulness (PU) (λ=-0.58, P=0.05≤0.05), thus H1.3.1 and
H1.3.3 are supported.
There is no statistically significant correlation, at the 95 percent confidence level,
between Tac and LE (λ=-0.47, P=0.34>0.05), hence H1.4.1 is rejected. On the contrary,
Tac has a direct positive effect on PE (λ=0.14, P<0.01), and perceived usefulness-PU
(λ=0.54, P=0.05≤0.05), hence H1.4.2-3 are supported.
The paths coefficients of Individual to perceived ease of use (Ind→PE: λ=-0.13,
P=0.07>0.05), and perceived usefulness (Ind→PU: λ=-0.14, P=0.17>0.05) are not sig-
nificant at 0.05 level, so H1.5.2-3 are refuted. However, Ind has indirect positive effects
on PE and PU via its influence on LE. On the other hand, it has a direct positive influ-
ence on learners’ e-learning self-efficacy (LE) (λ=0.35, P=0.00<0.01), hence H1.5.1 is
supported.
There is no statistically significant correlation between the subscale group of the
construct learning styles and PE (λ=-0.10, P=0.26>0.05), hence H1.6.2 is refuted. In
contrast, group has direct positive effects on learner's LE (λ=0.57, P=0.00<0.01) and
PU (λ=0.20, P=0.01 ≤0.05), hence H1.6.1 and H1.6.3 are supported.
The results also indicate the direct positive impact of LE on PE (LE→PE: λ=0.81,
P=0.00<0.01) and PU (LE→PU: λ=0.65, P=0.00<0.01), thus H2.1-2 are supported. In
addition, PE has a direct positive effect on PU (PE→PU: λ=0.18, P=0.03<0.05), thus
H3 is supported.
The results indicate that the model explained 82% of the variance in perceived use-
fulness when students study in an online environment (as indicated in Figure 2). The
determining factors which have direct positive effects on perceived usefulness are
ranked from the highest impact to the lower ones: learner’s e-learning self-efficacy
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(λ=0.65), tactile (λ=0.54), group (λ=0.20) and perceived ease of use (λ=0.18). On the
other hand, kinaesthetic has a direct negative influence on students' perceived useful-
ness of learning online.
Fig. 2. The results of the analysis of the correlation between the variables
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5 Discussion
In contrast to Lee et al. [23] finding, the results of the structural model of this study
reveal that the two components, namely Auditory and Visual of Learning styles are
excluded from the proposed research model, while the other four subscales of students'
learning styles, including Kinaesthetic, tactile, group and individual are statistically sig-
nificant in improving the model estimation fit. In this study, these factors have signifi-
cant effects on learners’ e-learning self-efficacy, perceived ease of use, and perceived
usefulness. These findings significantly differ from previous results reported in a study
by Al-Azawei et al. [22]. The discussion below highlights similarities and differentiates
the differences from other studies.
5.1 Learning styles and learners’ e-learning self-efficacy
The Kinaesthetic had a direct negative effect on learner’s e-learning self-efficacy.
This component is not really an element of the Index of Learning Styles which was
applied in previous studies presented in the literature review. However, Reid [24] fea-
tured it as "learn by doing something", or "participate in role-play", which we believe
that learners can still perform even in online platforms. Participants of this study did
not enjoy this style of learning when they study online. This can be inferred that online
learning is not apt for learning activities which require more physical interaction. This
finding has pedagogical implication for teachers in choosing appropriate online learn-
ing activities.
Surprisingly, tactile (preferences for "hands-on tasks" or making things) was per-
ceived positively to the surveyed participants. Other subscales of learning styles,
namely individual and group have positive influences on learners’ e-learning self-effi-
cacy. Although the learning occurs in online platforms, students still prefer to learn by
doing rather passive learning (through listening or audio style, for example). This can
be understood that students are in favour of hands-on tasks or collaborative and coop-
erative work, accompanying with practical instructions in order to ensure their self-
regulated learning. This result resonates with previous studies [21]-[24] where "active
learning" or "learning by doing" is students' favourite learning style. However, this
finding is not consistent with studies [25], [26]. In Baltaoğlu and Güven [29], most of
their participants were future teachers of languages and of primary schools, while the
participants of the study by Hendry et al. [30] were medical and dental students. These
kinds of majors seek to have physical interaction to have genuine experiences which
can be beneficial for their future jobs. This may explain the differences for their pre-
ferred learning styles and perceived self-efficacy.
Given students' online learning self-efficacy, they still value working in groups when
studying on the online learning system. This result is inconsistent with the studies by
Bakir et al. [50] where the students identified some major hindrances in terms of group
communication quality and group members' participation quantity and quality [52].
This finding implies that technology-related issues in online learning should be a matter
of concern, especially for learners who are quite new to this teaching and learning
method.
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5.2 Learning styles and perceived ease of use
Three components of LS, except Tac in this study, had no significant impact on PE.
This finding aligns with a study by Al-Azawei and Lundqvist [33], whereas is not sup-
ported by studies [31] [32]. Although the participants of the present study confirmed
their positive belief in their ability to study online, they did not find it easy to perform
well on this teaching and learning mode.
This finding once again urges educators and online education-service providers heed
the issue of online platform interface so that it will facilitate online learners during their
learning process.
5.3 Learning styles and perceived usefulness
In contradiction with a finding by Lee et al. [23], the kinaesthetic has a direct nega-
tive effect on learners’ e-learning self-efficacy. The difference may come from students'
demographics. Most of the participants in Lee et al. study, aging from 17 to 36, are
females (261 vs 140) while it is 191 vs 165, aging from 18-22 and majoring in IT,
business, and English language, in this study. It can be inferred that factors such as age
and disciplines probably influence their learning styles and the way they perceive the
significance of technology-based on their learning.
Similar to conclusions by Ahmed et al. [34], Felder and Brent [35], and Shamsuddin
and Kaur [36], the effects of students' learning styles on their Perceived Usefulness
were confirmed in this study. In particular, three subscales of LS, namely Tac, Gro, and
Kin, had both positive and negative direct effects on PU while Ind had an indirect in-
fluence on PU. This means that the participants acknowledged and refuted the im-
portant role online learning plays in their self-regulated learning process. Consequently,
it is implied that when students envision the appropriateness of their learning styles for
online learning tasks, they will acknowledge the benefits of this learning mode and vice
versa. More importantly, this result might be a helpful indicator for teachers as well as
academic management of educational institutions to consider learning style factors in
designing online tasks or diverse learning activities to meet students' expectations.
Also, for students with Kinaesthetic and Tactile learning styles who do not appreciate
the online learning mode, instructors need to heed their need of "doing activities" rather
than sensing or intuitive ones.
5.4 Learner's e-learning self-efficacy with perceived ease of use and perceived
usefulness
The study results demonstrated a significant positive effect of learners’ e-learning
self-efficacy on their perceived ease of use and perceived usefulness, and a direct pos-
itive influence of perceived ease of use on perceived usefulness. These results tied well
with previous studies [7], [9], [22], [38]-[40]. It can be inferred from these findings that
the more learners believe that they are able to study well in online platform, the more
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they acknowledge the importance and the feasibility of online learning. Therefore, de-
veloping learner self-efficacy which is characterized as a capacity [53] is essential in
online learning.
6 Conclusions and limitations
The present study aims to evaluate student-related factors, namely their learning
styles and perceived e-learning self-efficacy, on their perspectives on the usefulness of
online learning based on TAM model by Davis [6]. The proposed research model was
confirmed by SEM method. This study provides practical implications for universities,
educators, and instructors in implementing online courses which should be in harmony
with students' learning styles. Thus, it is essential that more emphasis should be placed
on diversifying online learning tasks. In addition, communicating the benefits of online
learning and promoting students' positive thinking about their self-efficacy during their
online learning process would enhance their active learning in the online learning en-
vironment.
The current study acknowledges three limitations. First, since the study employed
self-reported survey questionnaires, it suffered from the same limitations associated
with overestimation and/or underestimation of respondents, which is raised by Cole and
Gonyea [54]. Second, the data were collected from only a single private university in
Mekong Delta, Vietnam; hence, the generability of the findings to other contexts should
be cautious. Finally, this study utilized a single design, namely the quantitative method,
which limited its deeper understanding of students' explanation for their perceived use-
fulness of online learning.
7 Acknowledgement
We would like to express our sincere thanks to our colleagues and all of the partici-
pants for their assistance with this research.
8 References
[1] J. H. Wu, R. D. Tennyson, and T. L. Hsia, “A study of student satisfaction in a blended e-learning
system environment,” Computers & Education, vol. 55, no. 1, pp. 155–164, 2010. https://doi.org/
10.1016/j.compedu.2009.12.012
[2] M. Dennis, “The impact of MOOCs on higher education,” College and university, vol. 88, no. 2,
pp. 24-30, 2012.
[3] C. C. Tsai and C. C. Lin, “Taiwanese adolescents' perceptions and attitudes regarding the internet:
exploring gender differences,” Adolescence, vol. 39, no. 156, pp. 725-734, 2014.
[4] I. Ajzen, and M. Fishbein, Understanding attitudes and predicting social behavior. Prentice-Hall,
1980.
[5] M. Fishbein and I. Ajzen, Belief, attitude, intention, and behavior: An introduction to Theory and
Research. Addison-Wesley, 1975.
iJET ‒ Vol. 17, No. 15, 2022
137
Paper—Factors Affecting Students' Perspectives on the Usefulness of Learning Online
[6] F. D. Davis, “Perceived usefulness, perceived ease of use, and user acceptance of information
technology,” MIS Quarterly, vol. 13, no. 3, pp. 319-340, 1989. https://doi.org/10.2307/249008
[7] K. A. Pituch and Y. K. Lee, “The influence of system characteristics on e-learning use,” Comput-
ers & Education, vol. 47, no. 2, pp. 222–244, 2006. https://doi.org/10.1016/j.compedu.2004.
10.007
[8] H. M. Selim, “An empirical investigation of student acceptance of course websites,” Computers
& Education, 40(4), 343-360, 2003. https://doi.org/10.1016/S0360-1315(02)00142-2
[9] Yeou, M. (2016). An investigation of students’ acceptance of Moodle in a blended learning setting
using technology acceptance model. Journal of Educational Technology Systems, 44(3), 300-
318. https://doi.org/10.1177/0047239515618464
[10] F. Abd Majid and N. Mohd Shamsudin, “Identifying factors affecting acceptance of virtual real-
ity in classrooms based on Technology Acceptance Model (TAM),” Asian Journal of University
Education, vol. 15, no. 2, pp. 1-10, 2019.
[11] R. Awad, A. Aljaafreh, and A. Salameh, “Factors Affecting Students’ Continued Usage Inten-
tion of E-Learning During COVID-19 Pandemic: Extending Delone & Mclean IS Success
Model,” International Journal of Emerging Technologies in Learning (iJET), 17(10), pp. 120–
144, 2022. https://doi.org/10.3991/ijet.v17i10.30545
[12] H. M. Dai, T. Teo, N. A. Rappa, and F. Huang, “Explaining Chinese university students’ contin-
uance learning intention in the MOOC setting: A modified expectation confirmation model per-
spective,” Computers & Education, vol. 150, pp. 1-45, 2020. https://doi.org/10.1016/j.compedu.
2020.103850
[13] A. Ashrafi, A. Zareravasan, S. Rabiee Savoji, and M. Amani, “Exploring factors influencing stu-
dents’ continuance intention to use the learning management system (LMS): A multi-perspective
framework,” Interactive Learning Environments, pp. 1-23, 2020. https://doi.org/10.1080/10494
820.2020.1734028
[14] F. A. A. Eksail and E. Afari, “Factors affecting trainee teachers’ intention to use technology: A
structural equation modeling approach,” Education and Information Technologies, vol. 25, no. 4,
pp. 2681-2697, 2020. https://doi.org/10.1007/s10639-019-10086-2
[15] F. Aziz, R. M. Rasdi, A. A. M. Rami, F. Razali, and S. Ahrari, “Factors Determining Academics'
Behavioral Intention and Usage Behavior Towards Online Teaching Technologies During Covid-
19: An Extension of the UTAUT,” International Journal of Emerging Technologies in Learning
(iJET), vol. 17, no. 9, pp. 137-153, 2022.
[16] H. Ab Jalil, A. M. Ma’rof, and R. Omar, “Attitude and Behavioral Intention to Develop and Use
MOOCs among Academics,” International Journal of Emerging Technologies in Learning
(iJET), vol 14, no. 24, pp. 31–41, 2019. https://doi.org/10.3991/ijet.v14i24.12105
[17] Q. T. Pham and T. P. Tran (2018). Impact factors on using of e-learning system and learning
achievement of students at several universities in Vietnam. In International Conference on Com-
putational Science and Its Applications (pp. 394-409). Springer. https://doi.org/10.1007/978-3-
319-95171-3_31
[18] B. T. Khoa, N. M. Ha, T. V. H. Nguyen, and N. H. Bich, “Lecturers' adoption to use the online
Learning Management System (LMS): Empirical evidence from TAM2 model for Vi-
etnam,” ECONOMICS AND BUSINESS ADMINISTRATION, vol. 10, no. 1, pp. 3-17, 2020.
https://doi.org/10.46223/HCMCOUJS. econ.en.10.1.216.2020
[19] G. Maheshwari, “Factors affecting students’ intentions to undertake online learning: an empirical
study in Vietnam,” Education and Information Technologies, vol. 26, no. 6, pp. 6629-6649, 2021.
https://doi.org/10.1007/s10639-021-10465-8
[20] K. N. N. Tran, “The Adoption of Blended E-learning Technology in Vietnam using a Revision
of the Technology Acceptance Model,” Journal of Information Technology Education, vol.15,
pp. 253-282, 2016. https://www.informingscience.org/Publications/3522
138
http://www.i-jet.org
Paper—Factors Affecting Students' Perspectives on the Usefulness of Learning Online
[21] P. M. B. NGUYEN, Y. T. DO, and W. Y. WU, “Technology Acceptance Model and Factors
Affecting Acceptance of Social Media: An Empirical Study in Vietnam,” The Journal of Asian
Finance, Economics and Business, vol. 8, no. 6, pp. 1091-1099, 2021. https://doi.org/10.
13106/jafeb.2021.vol8.no6.1091
[22] A. Al-Azawei, P. Parslow, and K. Lundqvist, “Investigating the effect of learning styles in a
blended e-learning system: An extension of the technology acceptance model (TAM),” Austral-
asian Journal of Educational Technology, vol. 33, no. 2, pp. 1-23, 2017. https://doi.org/10.14742/
ajet.2741
[23] C. Lee, A. Yeung, and T. Ip, “Use of computer technology for English language learning: do
learning styles, gender, and age matter?” Computer-assisted language learning, vol. 29, no. 5, pp.
1035-1051, 2016. https://doi.org/10.1080/09588221.2016.1140655
[24] M. Reid, “The learning style preferences of ESL students,” TESOL Quarterly, vol. 21, no. 1, pp.
87-111, 1987. https://doi.org/10.2307/3586356
[25] C. R. West, J. H. Kahn, and M. M. Nauta, “Learning styles as predictors of self-efficacy and
interest in research: Implications for graduate research training,” Training and Education in Pro-
fessional Psychology, vol. 1, no. 3, pp. 174-183, 2007. https://doi.org/10.1037/1931-3918.1.3.174
[26] J. Dumbauld, M. Black, C. A. Depp, R. Daly, M. A. Curran, B. Winegarden, and D. V. Jeste,
“Association of Learning Styles with Research Self‐Efficacy: Study of Short‐Term Research
Training Program for Medical Students,” Clinical and translational science, vol. 7, no. 6, pp. 489-
492, 2014. https://doi.org/10.1111/cts.12197
[27] R. M. Felder and B. A. Soloman (2000). Index of Learning Styles. Retrieved from:
http://www.engr.ncsu.edu/learningstyles/ilsweb.html
[28] I. Direito, A. Pereira, and A. M. de Oliveira Duarte, “Engineering undergraduates’ perceptions of
soft skills: Relations with self-efficacy and learning styles, ” Procedia-Social and Behavioral Sci-
ences, vol. 55, pp. 843-851, 2012. https://doi.org/10.1016/j.sbspro.2012.09.571
[29] M. G. Baltaoğlu and M. Güven, “Relationship between self-efficacy, learning strategies, and
learning styles of teacher candidates (Anadolu University example),” South African Journal of
Education, vol. 39, no. 2, 2019. https://doi.org/10.15700/saje.v39n2a1579
[30] G. D. Hendry, P. Heinrich, P. M. Lyon, A. L. Barratt, J. M. Simpson, S. J. Hyde, and S. Mgaieth,
“Helping students understand their learning styles: Effects on study self‐efficacy, preference for
group work, and group climate,” Educational Psychology, vol. 25, no. 4, pp. 395-407, 2005.
https://doi.org/10.1080/01443410500041706
[31] V. C. Gu, J. Triche, M. A. Thompson, and Q. Cao, “Relationship between learning styles and
effectiveness of online learning systems,” International Journal of Information and Operations
Management Education, vol. 5, no. 1, pp. 32-47, 2012. https://doi.org/10.1504/IJIOME.2012.
051600
[32] H. K. Lu, P. C. Lin, and A. N. Chen, “An empirical study of behavioral intention model: Using
learning and teaching styles as individual differences,” Journal of Discrete Mathematical Sci-
ences and Cryptography, vol. 20, no. 1, pp. 19-41, 2017. https://doi.org/10.1080/09720529.
2016.1177968
[33] A. Al-Azawei and K. Lundqvist, “Learner differences in perceived satisfaction of an online learn-
ing: An extension to the Technology Acceptance Model in an Arabic sample,” Electronic Journal
of E-Learning, vol. 13, no. 5, pp. 408-426, 2015. https://academic-publishing.org/index.php/ejel/
article/view/1942
[34] J. Ahmed, K. SHAH, and N. Shenoy, “How different are students and their learning styles?” In-
ternational Journal of Research in Medical Sciences, vol. 1, no. 3, pp. 212-215, 2013.
https://doi.org/10.5455/2320-6012.ijrms20130808
[35] R. M. Felder and R. Brent, “Understanding student differences,” Journal of Engineering Educa-
tion, vol. 94, no. 1, pp. 57–72, 2005. https://doi.org/10.1002/j.2168-9830.2005.tb00829.x
iJET ‒ Vol. 17, No. 15, 2022
139
Paper—Factors Affecting Students' Perspectives on the Usefulness of Learning Online
[36] N. Shamsuddin and J. Kaur, “Students' Learning Style and Its Effect on Blended Learning, Does
It Matter? International Journal of Evaluation and Research in Education, vol. 9, no. 1, pp. 195-
202, 2020.
[37] R. E. Mayer, “Does styles research have useful implications for educational practice?” Learning
and Individual Differences, vol. 21, no. 3, pp. 319–320, 2011. https://doi.org/10.1016/j.lindif.20
10.11.016
[38] N. T. T. Ho, S. Sivapalan, H. H. Pham, L. T. M. Nguyen, A. T. Van Pham, and H. V. Dinh,
“Students' adoption of e-learning in emergency situation: the case of a Vietnamese university
during COVID-19,” Interactive Technology and Smart Education, vol. 17, no. 4, pp. 1–24, 2020.
https://doi.org/10.1108/ITSE-08-2020-0164
[39] W. Hong, J. Y. Thong, W. M. Wong, and K. Y. Tam, “Determinants of user acceptance of digital
libraries: An empirical examination of individual differences and system characteristics,” Journal
of Management Information Systems, vol. 18, no. 3, pp. 97–124, 2002. https://doi.org/10.
1080/07421222.2002.11045692
[40] S. Y. Park, “An analysis of the technology acceptance model in understanding university students'
behavioral intention to use e-learning,” Journal of Educational Technology & Society, vol. 12,
no. 3, pp. 150-162, 2009. https://www.jstor.org/stable/jeductechsoci.12.3.150
[41] C. S. Ong and J. Y. Lai, “Gender differences in perceptions and relationships among dominants
of e-learning acceptance,” Computers in Human Behavior, vol. 22, no. 5, pp. 816–829, 2006.
[42] H. Nguyen, H. Pham, N. Vu, and H. Hoang, “Factors Influencing Students' Intention to Use E-
learning System: A Case Study Conducted in Vietnam,” International Journal of Emerging Tech-
nologies in Learning (iJET), vol. 15, no. 18, pp. 165-182, 2020. https://doi.org/10.3991/ijet.
v15i18.15441
[43] F. D. Davis, “A technology acceptance model for empirically testing new end-user information
systems: Theory and results,” PhD Thesis, Massachusetts Institute of Technology, Cambridge,
MA, 1985.
[44] S. Al-Gahtani, “Empirical investigation of e-learning acceptance and assimilation: A structural
equation model,” Applied Computing and Informatics, vol. 12, no. 1, pp. 27–50, 2016.
https://doi.org/10.1016/j.aci.2014.09.001
[45] A. Tarhini, M. Hassouna, M. S. Abbasi, and J. Orozco, “Towards the Acceptance of RSS to Sup-
port Learning: An empirical study to validate the Technology Acceptance Model in Leba-
non,” Electronic Journal of e-Learning, vol. 13, no. 1, pp. 30-41, 2015. http://www.ejel.org/
main.html
[46] I. AlYoussef, “An empirical investigation on students’ acceptance of (SM) use for teaching and
learning,” International Journal of Emerging Technologies in Learning (iJET), vol 15, no. 4, pp.
158-178, 2020. https://doi.org/10.3991/ijet.v15i04.11660
[47] R. V. Krejcie and D. W. Morgan, “Determining sample size for research activities ” Educational
and psychological measurement, vol. 30, no. 3, pp. 607-610, 1970. https://doi.org/10.1177/
001316447003000308
[48] J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, Eighth
ed. Cengage Learning EMEA, 2019.
[49] D. W. Gerbing and J. C. Anderson, “An updated paradigm for scale development incorporating
unidimensionality and its assessment,” Journal of marketing research, vol. 22, no. 2, pp. 186-
192, 1988. https://doi.org/10.1177/002224378802500207
[50] N. Bakir, S. Humpherys, and K. Dana, “Students' Perceptions of Challenges and Solutions to
Face-to-Face and Online Group Work,” Information Systems Education Journal, vol. 18, no. 5,
pp. 75-88, 2020.
[51] C. Fornell and D. F. Larcker, “Structural Equation Models with Unobservable Variables and
Measurement Error: Algebra and Statistics,” Journal of Marketing Research. vol. 18, no. 3, pp.
382-388, 1981. https://doi.org/10.1177/002224378101800313
140
http://www.i-jet.org
Paper—Factors Affecting Students' Perspectives on the Usefulness of Learning Online
[52] B. Chang and H. Kang, “Challenges facing group work online,” Distance Education, vol. 37, no.
1, pp. 73-88, 2016. https://doi.org/10.1080/01587919.2016.1154781
[53] L. M. Blaschke and S. Hase (2016). Heutagogy: A holistic framework for creating twenty-first-
century self-determined learners. In The future of ubiquitous learning (pp. 25-40). Springer.
[54] J. S. Cole and R. M. Gonyea, “Accuracy of self-reported SAT and ACT test scores: Implications
for research,” Research in Higher Education, vol. 51, no. 4, pp. 305-319, 2010. https://doi.org/
10.1007/s11162-009-9160-9
9 Authors
Tuong Cao Dinh is an English instructor at English Language Department, FPT
University, Can Tho, Vietnam. His current research interests span the application of
online and blended learning modes to EFL students, with a particular focus on learning
self-regulation.
Dr. Sang Minh Vo is a lecturer of the Business Administration Department, FPT
University, Can Tho campus. His main research areas are marketing, branding, cus-
tomer experience, customer behavior, economic and management.
Article submitted 2022-06-15. Resubmitted 2022-07-05. Final acceptance 2022-07-07. Final version pub-
lished as submitted by the authors.
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