Content uploaded by Hermi Zaswita
Author content
All content in this area was uploaded by Hermi Zaswita on Jun 22, 2023
Content may be subject to copyright.
Available via license: CC BY 4.0
Content may be subject to copyright.
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
Analysis of Factors Affecting Vocational Students'
Intentions to Use a Virtual Laboratory Based on the
Technology Acceptance Model
https://doi.org/10.3991/ijim.v17i12.38627
Doni Tri Putra Yanto1(), Sukardi1, Maryatun Kabatiah2, Hermi Zaswita3,
Oriza Candra1
1 Universitas Negeri Padang, Sumatera Barat, Indonesia
2 Universitas Negeri Medan, Sumatera Utara, Indonesia
3 STKIP Muhammadiyah Sungai Penuh, Jambi, Indonesia
donitriputra@ft.unp.ac.id
Abstract—Virtual Laboratory (VL) is increasingly being used in the learning
process, offering a variety of options. However, when choosing the appropriate
type of VL, it is necessary to consider the factors that affect the successful im-
plementation of the VL. This study discusses the factor analysis that affects the
vocational students' intention to use a VL in remote learning. Based on the Tech-
nology Acceptance Model (TAM), Perceived Ease of Use (PEU), Perceived Use-
fulness (PU), and Attitude towards VL (A) are factors that influence people's in-
tention to use technology. This research was conducted on the learning process
of the Power Electronics Practicum for Vocational students. This study involved
105 vocational students from the Industrial Electrical Engineering Study Program
at Universitas Negeri Padang. A quantitative survey-based approach using a
questionnaire was applied to obtain the research data. The research data was an-
alyzed using Partial Least Square-Structural Equation Modeling (PLS-SEM).
The results showed that all factors (PEU, PU, & A) had a significant and positive
direct and indirect effect (through intervening variables) on the vocational stu-
dents' intention to use VL to support remote learning. These factors can be con-
sidered in determining the appropriate virtual laboratory application to be imple-
mented in the learning process.
Keywords—technology acceptance model, virtual laboratory, student inten-
tions to use, remote learning, vocational students
1 Introduction
Vocational education (VE) is a specific form of education that focuses on providing
students with practical skills and knowledge to prepare them for work in specific in-
dustries. Therefore, practical learning in the laboratory is more dominant than theoret-
ical learning. The practical learning process in the laboratory provides students with
hands-on learning experiences, allowing them to interact with materials and observe
94
http://www.i-jim.org
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
theoretical concepts in action [1], [2]. Practical learning aims to develop students' abil-
ities and application skills, such as observing, measuring, planning, estimating, devel-
oping hypotheses, obtaining data, problem-solving, collaborating, interpreting results,
and time management [2]–[4]. However, providing physical or real laboratories can be
challenging for educational institutions due to the high investment costs for equipment
and materials, as well as the time required for preparation [5]–[7]. Moreover, practical
learning in the laboratory is not conducive to remote learning [8], [9].
The COVID-19 pandemic that began in 2020 has further hindered the implementa-
tion of practical learning in real laboratories due to restrictions on face-to-face interac-
tions to prevent the spread of COVID-19 [8], [10]. As a result, remote learning has been
implemented in every educational institution, including the implementation of practical
learning [11], [12]. Remote learning has become increasingly popular during the
COVID-19 pandemic era, leading to the development of various models, methods, ap-
proaches, and learning media for practical learning [13]–[15]. One such approach is the
use of the virtual laboratory (VL), which provides easily accessible and flexible labor-
atory applications without the limitations of physical space and time. The VL simulates
real laboratory environments through various computer software, allowing students to
conduct experiments independently. Thus, students can comfortably design experi-
ments, analyze and interpret data, and explore various concepts within a virtual envi-
ronment [5], [9].
The implementation of VL in practical learning processes significantly contributes
to achieving learning objectives and outcomes in remote learning and blended learning
(a combination of face-to-face learning in a real laboratory with remote learning using
VL) [16]–[18]. Moreover, the use of VL creates a different learning atmosphere that
increases students' interest and motivation in learning. However, to benefit optimally
from VL in the learning process, special care and consideration are necessary when
selecting the type of VL technology [19], [20]. Previous studies have revealed that sev-
eral cases of VL implementation were not optimal due to errors in selecting the type of
VL. One of the indicators is the low intention of students to use the VL chosen by the
lecturer. Consequently, the implementation of VL not being optimal, and the benefits
offered by VL could not be fully realized [16], [20]. For that reason, lecturers need to
comprehend the factors that influence students' intention to use or not use VL technol-
ogy. Such comprehension will help ensure that VL is appropriately adopted and utilized
by all students, enabling the effectiveness of practical learning implementation using
VL to be optimal. Currently, there is a lack of references and research outcomes that
specifically discuss the factors that influence students' intentions to use VL in the practi-
cum learning process.
Based on the technology acceptance model (TAM), students' intention to use VL can
be defined as Behavioral Intention (BI). In addition, perceived usefulness (PU) and
perceived ease of use (PEU) are two important factors that influence BI [19], [21], [22].
These factors also have a direct influence on attitudes towards technology use (A),
which also have a positive and significant influence on students' BI to use VL in prac-
tical learning [1], [19], [20]. Therefore, the TAM model was chosen as a framework for
investigating the factors that influence vocational students' intention to use a VL in the
learning process.
iJIM ‒ Vol. 17, No. 12, 2023
95
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
The research questions for this study are: (1) What are the factors that influence
vocational students' intention to use VL in remote learning, particularly in the process
of power electronics practical learning, based on the TAM theory? (2) Are these factors
positively and significantly influential? Therefore, the general objective of this study is
to analyze the factors that influence vocational students' intention to use VL in the
power electronics practicum learning process in remote learning. This study can con-
tribute to the literature for educators and instructors about the factors that need to be
considered in determining the appropriate type of VL to be applied in practical learning
processes. The specific objectives of this study are to analyze: (1) the direct effect of
PU on BI; (2) the direct effect of PEU on BI; (3) the direct effect of A on BI; (4) the
direct effect of PU on A; (5) the direct effect of PEU on A; (6) the direct effect of PEU
on PU; (7) the indirect effect of PU on BI through A as intervening variables; (8) the
indirect effect of PEU on BI through A as intervening variables.
The study has several important implications. Firstly, it provides a better understand-
ing of the factors that influence students' intentions to use VL applications. Secondly,
this study shows that the factors that influence students' intentions to use VL applica-
tions are interrelated and influence each other. Thereupon, developers of VL applica-
tions must consider these factors in an integrated and comprehensive manner when de-
signing, developing, and promoting VL applications. Thirdly, this research can also
assist educational institutions and lecturers in selecting appropriate VL applications for
use in practical learning. By considering the factors that influence students' intentions
to use VL, lecturers can choose VL applications that are more suitable and effective in
enhancing student learning.
2 Literature review
2.1 Vocational student
Vocational education is a type of education that focuses on providing students with
specific practical skills and knowledge required for employment in certain industries
[22], [23]. The primary goal of vocational education is to equip students with the skills
and practical knowledge necessary to work in a particular field [24]–[26]. Vocational
students are those who are enrolled in vocational education programs and undertake
courses that focus on developing practical skills and knowledge in specific fields. They
pursue expertise in their chosen fields, such as automotive mechanics, information tech-
nology, healthcare, and others [22], [25], [26]. Vocational students acquire practical
skills through hands-on training and internships, allowing them to develop the skills
necessary to work in specific industries. They have excellent employment prospects
due to their practical and specific educational backgrounds. They possess industry-rel-
evant skills and knowledge and are job-ready upon graduation [23], [27], [28].
96
http://www.i-jim.org
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
2.2 Virtual laboratory
A virtual laboratory is a learning platform that allows users to perform laboratory
experiments or practices in a virtual environment that closely resembles a physical en-
vironment. In a VL, users can simulate, observe, and interactively analyze experimental
results safely without requiring actual laboratory materials or equipment [20], [29],
[30]. The advantages of using VL in the learning process are as follows: (1) Time and
place flexibility, as students can access the VL from anywhere and at any time without
being tied to a strict laboratory schedule; (2) Cost-effectiveness, as in the VL, students
do not need to buy or use actual laboratory materials and equipment, thus saving costs
and reducing the risk of accidents; (3) Interactivity, as VL allows students to conduct
interactive experiments and simulations, thereby increasing their engagement and un-
derstanding of the concepts being taught; (4) Safety, as students can conduct experi-
ments and simulations safely without any danger or risks that may occur in the physical
laboratory [31]–[33]. However, VL has some limitations, including the lack of hands-
on experience which is crucial for certain scientific disciplines. Additionally, VL may
have limited availability of equipment and materials, technical difficulties such as soft-
ware crashes or internet connectivity issues, limited interaction, limited collaboration,
and may lack authenticity in replicating real laboratory environments, which can impact
the validity and reliability of experimental results obtained [31], [32], [34]. Despite
these limitations, VL is a valuable tool for teaching and learning scientific concepts,
and their use can be optimized by carefully considering the advantages and limitations,
as well as choosing appropriate virtual laboratory applications based on the specific
learning objectives and needs of the users [31], [32], [35].
2.3 Technology acceptance model
The Technology Acceptance Model (TAM) is a theory that explains the intention of
individuals to use technology (BI) [22], [36]. This theory posits that an individual's
intention to use technology is determined by two primary factors: perceived usefulness
(PU) and perceived ease of use (PEU) [21], [22]. PU refers to the degree to which an
individual believes that technology can provide benefits in their personal or profes-
sional life, while PEU refers to the degree to which an individual feels that technology
is easy to use. According to TAM, the higher the PU and PEU, the greater the likelihood
of an individual using it. Additionally, factors such as attitudes toward technology (A)
and environmental conditions can also impact the intention to use technology. TAM
has been widely employed in various studies on technology acceptance, particularly in
the context of education and learning [10], [21]. Therefore, this study applied TAM to
analyze the factors affecting vocational students' intention to use the VL.
2.4 Vocational students' intentions to use a virtual laboratory
The student's intention to use a VL refers to their willingness to use a VL in the
learning process. The higher the students' intention to use a VL, the more optimal the
implementation of a VL in the learning process will be. Therefore, it is important to
iJIM ‒ Vol. 17, No. 12, 2023
97
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
consider students' intention to use a VL to optimize its implementation in the learning
process. Based on TAM, students' intention to use VL is defined as BI, which is a per-
son's intention to use and apply technology [19], [20]. In addition, PU and PEU are two
important factors that influence BI. PU refers to the perceptions of vocational students
regarding the benefits of using VL in remote learning [19], [36]. PEU refers to the per-
ceptions of vocational students regarding the ease of use of VL in remote learning. PEU
directly affects PU [19], [20]. Meanwhile, A refers to the attitude of vocational students
towards the use of VL technology in remote learning, which is considered one of the
important components in predicting BI [36], [37].
3 Methods
3.1 Research design
The study adopts a non-experimental [19], explanatory [38], and descriptive ap-
proach with a quantitative methodology [19]. In other words, a survey-based quantita-
tive approach was utilized in this study. The survey method is a systematic collection
of information that enables researchers to obtain accurate information on problems and
the relationships between variables. It provides descriptive answers and reveals the in-
fluence between variables [39]. The research variables for this study are PU, PEU, A,
and BI, and are illustrated in Figure 1, which represents the research model.
Fig. 1. Initial Research Model
3.2 Population and sample
The population for this study comprised 142 third-year vocational students who were
enrolled in the power electronics course under the industrial electrical engineering
study program, faculty of engineering, at Universitas Negeri Padang. The sample size
was determined using the Slovin formula, which resulted in a sample of 105 students
[40]. A simple random sampling technique was used for the selection of participants in
this study [41].
98
http://www.i-jim.org
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
3.3 Research instruments
The data collection technique used is a survey method using a research instrument.
The data collection instrument used is a questionnaire with a Likert scale ranging from
1 to 5. The indicators for the questionnaire were obtained through a literature review
and are presented in Table 1. This study uses reflective indicators where the indicators
reflect the construct or variable [41]. PU has 4 indicators [22], [36], PEU has 5 indica-
tors [21], [22], A has 4 indicators [19], [31], [36], and BI has 5 indicators [10], [19],
[36].
Table 1. Dimensions and Indicators of Research Instrument
Dimensions
Theoretical
Framework
Indicator
Perceived Ease of
Use [21], [22]
PEU.1. The VL used is easy to use.
PEU.2. The VL used is easy to learn.
PEU.3. The VL used is easy to access.
PEU.4. The VL used is easy to understand.
PEU.5. The VL used is convenient.
Perceived Useful-
ness [22], [36]
PU.1. The VL used helps to save time.
PU.2. The VL used helps me to be self-reliable.
PU.3. The VL used helps to improve my knowledge.
PU.4. The VL used helps to improve my performance.
Attitudes Toward
Use [19], [31], [36]
A.1. The VL used is enjoyable.
A.2. I am pleased enough with the VL used.
A.3. I am satisfied with the performance of the VL used.
A.4. The VL used is pleasant to me
Behavioral Inten-
tion [10], [19], [36]
BI.1. I tend to use VL which is applied in the learning process.
BI.2. I believe that the use of VL in the learning process is avail-
able
BI.3. I plan to use VL in the learning process.
BI.4. I will use VL if available in the learning process.
BI.5. I have a strong intention to use VL in the learning process.
3.4 Technique of data analysis
The research data collected using research instruments were analyzed to determine
research findings in line with the research objectives. Variant-based Structural Equation
Modeling (SEM) was employed to analyze the research data [5], [40], [41]. PLS-SEM
analysis was conducted using the SmartPLS application to determine the direct and
indirect effects of several exogenous variables on endogenous variables. This allowed
for the identification of the factors that influence vocational students' intention to use
the VL in the practical learning process of Power Electronics courses. Before this, a
validity analysis of variables and instrument indicators was also included in the PLS-
SEM analysis.
iJIM ‒ Vol. 17, No. 12, 2023
99
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
4 Results and discussion
4.1 Results
This study employs the PLS-SEM model to investigate the factors affecting the vo-
cational education students' intention to use a VL in the practical learning process of
the power electronics course. Specifically, this study uncovers the direct, indirect, and
total effects of PU, PEU, and A on BI. The direct effect in this study includes the effect
of PU & PEU on A, the effect of PU & PEU on BI, and the effect of A on BI. Mean-
while, the indirect effect is the impact of PU and PEU on BI through A as an intervening
variable. The significance of each influence is also analyzed. The initial PLS-SEM
model, which was compiled based on the literature review of each research variable, is
presented in Figure 2.
Fig. 2. Initial PLS-SEM Model
The Preliminary Research Model is analyzed to ensure that each indicator meets the
assumptions and requirements of the analysis. There is no problem of multicollinearity,
which is an assumption that must be met in the inner and outer model analysis. Multi-
collinearity is indicated by the Variance Inflating Factor (VIF) value exceeding 5. If the
VIF value is greater than 5, then a multicollinearity problem occurs. Otherwise, if the
VIF value is less than 5, there is no multicollinearity problem [42], [43]. Table 2 pre-
sents the results of the Outer VIF Values analysis.
100
http://www.i-jim.org
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
Table 2. The Outer VIF Values Analysis
PU
PEU
A
BI
Indicator
VIF
Indicator
VIF
Indicator
VIF
Indicator
VIF
PU.1
1.678
PEU.1
2.387
A.1
1.532
BI.1
1.575
PU.2
2.017
PEU.2
5.418
A.2
1.724
BI.2
1.487
PU.3
1.654
PEU.3
2.060
A.3
1.402
BI.3
1.545
PU.4
2.477
PEU.4
5.628
A.4
1.387
BI.4
1.580
PEU.5
1.855
BI.5
1.371
The results of the outer VIF Value analysis indicate that two indicators, PEU.2 and
PEU.4, have VIF values above 5 (VIF > 5), indicating the presence of multicollinearity.
Therefore, the two indicators need to be eliminated, and a second analysis is conducted
for the Outer VIF Values as presented in Table 3.
Table 3. The Second Outer VIF Values Analysis
PU
PEU
A
BI
Indicator
VIF
Indicator
VIF
Indicator
VIF
Indicator
VIF
PU.1
1.678
PEU.1
2.454
A.1
1.532
BI.1
1.575
PU.2
2.017
PEU.3
1.613
A.2
1.724
BI.2
1.487
PU.3
1.654
PEU.5
2.320
A.3
1.402
BI.3
1.545
PU.4
2.477
A.4
1.387
BI.4
1.580
BI.5
1.371
The results of the second outer VIF Value analysis in Table 3 show that all VIF
values are already below 5 (VIF <5). Therefore, it can be concluded that there is no
multicollinearity problem. The next step is to test multicollinearity for latent variables
(inner model). As with measuring indicators (outer models), inner models must also be
ensured that they do not have multicollinearity between variables. Table 4 presents the
results of the inner VIF value analysis.
Table 4. The Inner VIF Values Analysis
A
BI
PU
1.220
2.342
PEU
1.294
1,847
A
1.278
The Inner VIF Value analysis in Table 3 indicates that all VIF values are 5 (VIF <5).
Therefore, it can be concluded that there is no multicollinearity problem among the
latent variables. The next step is to analyze the Goodness of Fit (GoF) criteria to ensure
that compiled model meets the required standards. The GoF analysis results for the
models used in this study are presented in Table 5.
iJIM ‒ Vol. 17, No. 12, 2023
101
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
Table 5. The GoF Analysis Results
SRMR
< 0,08
[41], [42]
NFI
> 0,9
[41], [42]
Rms theta
< 0,102
[41], [42]
GoF
Saturated Model
0,057
0,993
0,079
Fit
Estimated Model
0,057
0,993
0,079
Fit
The results of the GoF analysis show that the research model has met the GoF crite-
ria, where the Standardized Root Mean Square Residual (SRMR) value is 0.067, The
Normed Fit Index (NFI) value is 0.989, and the Root Mean Square Theta (RMS Theta)
value is 0.049 [42], [44]. Therefore, based on these three values, the path model has
met the GoF criteria. The PLS-SEM analysis results of the final research model are
presented in Figure 3.
Fig. 3. The PLS-SEM Analysis Results of the Final Research Model
Indicator measurements (Outer model). The results of the construct validity and
reliability analysis are presented in Table 6. Internal Consistency Reliability (ICR) is
an analysis used to determine the ability of indicators to measure variables or their latent
variables[41], [42]. In PLS-SEM, ICR is indicated by the values of composite reliability
and Cronbach's alpha. Table 6 shows that all variables have a Cronbach's Alpha value
greater than 0.6 and a composite reliability value greater than 0.7, indicating that all
variables meet the ICR criteria (reliable) [41], [42]. A unidimensionality test is also
required to ensure that there are no problems in the measurement, which is indicated by
the composite reliability and Cronbach's alpha value [37], [42], [44]. Table 6 shows
that all variables meet the unidimensionality criteria, with composite reliability and
Cronbach's alpha greater than 0.7 [42], [44]. The next aspect to consider is convergent
validity (CV). CV for variables that have reflective indicators is evaluated based on the
AVE value. Table 6 presents that the AVE value for each variable is greater than 0.50,
indicating that all variables are valid and meet the convergent validity criteria [42], [44].
102
http://www.i-jim.org
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
Table 6. Construct Reliability and Validity Analysis
Cronbach
's Alpha
(> 0,7)
rho_A
(> 0,7)
Composite
Reliability
(> 0,7)
AVE
(> 0,5)
Internal Con-
sistency Reli-
ability
Unidimen-
sionalitas
Model
Convergent
Validity
PU
0.714
0.748
0.708
0.633
Reliable
Reliable
Valid
PEU
0.706
0.815
0.756
0.612
Reliable
Reliable
Valid
A
0.757
0.725
0.716
0.528
Reliable
Reliable
Valid
BI
0.774
0.796
0.729
0.577
Reliable
Reliable
Valid
Inner model analysis. The analysis of the inner model is conducted to determine
the relationship between variables and to reveal the direct effect (path coefficient), in-
direct effect, and total effect. Moreover, it is used to determine the simultaneous effect
of exogenous variables based on the R Square, and Adjusted R Square [41], [42]. The
results of the analysis of latent variables or inner model analysis are presented in Table
7.
Table 7. The Inner Model Analysis
A (Y)
BI (Z)
R
Square
R Square
Adjusted
Path
Coeff.
Ind. Effects
Tot. Effects
Path
Coeff.
Ind.
Effects
Total
Effect
PU
0.570
-
0.570
0.239
0.503
0.742
-
-
PEU
0.426
-
0.426
0.231
0.437
0.668
-
-
A
-
-
-
0.629
-
0.629
0.609
0.604
BI
-
-
-
-
-
-
0.747
0.742
Direct effect analysis. In PLS-SEM analysis, the values of direct effects can be ob-
served in the path coefficient. Path coefficient values range from -1 to +1, with values
closer to +1 indicating a stronger and more positive relationship between the two con-
structs, while values closer to -1 indicate a negative effect [41], [42]. Table 7 presents
that: (1) The direct effect of PU on A is 0.570 (Path Coefficient = 0.570) and the effect
is positive, meaning that if PU increases by one unit, A will also increase by 57.0%; (2)
The direct effect of PEU on A is 0.426 (Path Coefficient = 0.426) and the effect is
positive, meaning that if PEU increases by one unit, A will also increase by 42.6%; (3)
The direct effect of PU on BI is 0.239 (Path Coefficient = 0.239) and the effect is pos-
itive, meaning that if PU increases by one unit, BI can also increase by 23.9%; (4) The
direct effect of PEU on BI is 0.231 (Path Coefficient = 0.231) and the effect is positive,
meaning that if PEU increases by one unit, BI can also increase by 23.1%; and (5) The
direct effect of A on BI is 0.629 (Path Coefficient = 0.629) and the effect is positive,
meaning that if A increases by one unit, BI can also increase by 62.9%.
Indirect effect analysis. Indirect effects are the effects of an exogenous variable on
an endogenous variable through an intervening variable. In PLS-SEM analysis, as well
as the Path Coefficient, the indirect effect values range from -1 to +1, with values closer
to +1 indicating a stronger and more positive relationship between the two constructs,
and values closer to -1 indicating a negative relationship [41]–[43]. Table 7 shows that:
(1) The indirect effect of PU on BI through A as an intervening variable is 0.503, which
iJIM ‒ Vol. 17, No. 12, 2023
103
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
means that if PU increases by one unit, BI can indirectly increase through A by 50.3%;
and (2) The indirect effect of PEU on BI through A as an intervening variable is 0.437,
which means that if PEU increases by one unit, BI can indirectly increase through A by
43.7%.
Total effect analysis. Total effects are the sum of indirect and direct effects. In this
study, the total influence analyzed is the total influence of PU on BI and the total influ-
ence of PEU on BI. So, it is known how much PU and PEU variables can affect BI in
total [41]–[43]. Table 7 shows that: (1) The total influence of PU on BI is 0.742, which
means that if PU increases by one unit, BI can increase either directly or indirectly
through A as the intervening variable by 74.2%; and (2) The total influence of PEU on
BI is 0.668, which means that if PEU increases by one unit, BI can increase either
directly or indirectly by 66.8%.
R square and adjusted square analysis. The R Square value is between 0 and 1, with
values greater than 0.67 considered the strong category, values between 0.33 and 0.67
considered the moderate category, and below 0.33 considered the weak category) [41],
[42]. The Adjusted R Square value is used as a reference to assess the ability of exog-
enous latent variables to influence endogenous latent variables. Table 7 shows that: (1)
The simultaneous influence of PU and PEU on A is 0.609 (R Square is 0.609) and the
adjusted r square value is 0.604, which means that PEU and PU simultaneously affect
A by 0.604 or 60.4% (moderate category); (2) The simultaneous effect of PEU, PU,
and A on BI is 0.747 (R Square is 0.747) and the adjusted r square value is 0.742, which
mean that PEU, PU, and A variables simultaneously affect BI by 0.742 or 74.2% (strong
category).
Significance analysis. The significance analysis aims to determine whether the ef-
fects obtained through the analysis of the Inner Model are significant or insignificant.
Specifically, it is done to determine the significance of each effect of exogenous latent
variables on endogenous latent variables. The results of the significance analysis in
Table 8 show that the overall significance value of each effect is less than 0.05 (P value
<0.05), indicating that all the tested influences in this study are significant.
Table 8. The Results of Significance Analysis
Path Coefficients/ Direct effect
Original Sample (O)
Sample Mean (M)
P Values
PU → A
0.570
0.575
0.000
PU → BI
0.196
0.202
0.019
PEU → A
0.426
0.420
0.000
PEU → BI
0.178
0.185
0.007
A → BI
0.629
0.616
0.000
Indirect Effect
PU → A → BI
0.358
0.354
0.010
PEU →A → BI
0.268
0.259
0.008
Total Effect
PU → BI
0.554
0.556
0.006
PEU → BI
0.681
0.688
0.020
104
http://www.i-jim.org
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
4.2 Discussion
Perceived Usefulness (PU). The analysis results demonstrated that PU had a direct
and significant effect on A, with a positive influence of 57.0% (moderate category).
Additionally, PU had a positive and significant effect on BI, with a direct effect of 23.9
(weak category) and an indirect effect of 50.3% (moderate category) through A, acting
as an intervening variable. Therefore, the total impact of PU on BI was 74.2% (strong
category). These findings are consistent with prior research, which also identified the
positive and significant influence of technology's perceived usefulness on attitudes to-
ward its use and intentions to use the technology [22], [36], [43], [45]. In the context of
using VL for learning, these outcomes suggest that PU exerts a positive and significant
impact on vocational students' intention to use VL for learning. The results also high-
light the importance of focusing on the perceived benefits of using VL to increase its
adoption among vocational students. By offering a useful VL application, vocational
students will develop a more favorable attitude toward its use and higher intentions to
use it in the learning process.
Perceived Ease of Use (PEU). The results of the analysis showed that PEU had a
direct and significant effect on A, with a positive influence of 42.6% (moderate cate-
gory). PU also had a positive and significant effect on BI, with a direct effect of 23.1
(weak category) and an indirect effect through A as an intervening variable of 43.7%
(moderate category). Therefore, the total influence of PU on BI was 66.8% (strong cat-
egory). The results showed that perceived ease of use had a significant effect on the
attitude towards the use of technology and behavioral intention among vocational stu-
dents in the Industrial Electrical Engineering Study Program. This is in line with previ-
ous research, which also demonstrates the positive and significant effects of the ease of
using technology on attitudes towards technology use and intention to use technology
[17], [46], [47]. In the case of using VL in the learning process, this result indicates that
PU has a positive and significant influence on the intention of vocational students to
use VL in the learning process. Furthermore, the results showed that to increase VL
adoption in the learning process among vocational students, it is important to focus on
the ease of using the VL application. By providing VL applications that are easy to use,
vocational students will have a more positive attitude towards the use of VL and higher
intentions to use it.
Attitudes towards technology use. The results of the analysis showed that A had a
direct and significant effect on BI, with a positive influence of 62.9% (medium cate-
gory). The direct effect of A on BI is larger when compared to the direct impact of PU
and PEU on BI. However, A is influenced by PU and PEU, meaning that the strength
of the influence of A on BI is also influenced by PU and PEU. The results found that
attitudes toward use had a significant effect on behavioral intention among vocational
students in the Industrial Electrical Engineering Study Program. This is in accordance
with previous research, which also shows the positive and significant influence of a
person's attitude towards technology on the intention to use the technology [1], [10],
[19]. Attitudes themselves are influenced by the previous two factors, namely perceived
usefulness, and perceived ease of use. [1], [22] In the case of the use of VL in the
learning process, this result shows that A has a positive and significant influence on the
iJIM ‒ Vol. 17, No. 12, 2023
105
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
intention of vocational students to use VL in the learning process. The results also
showed that to increase VL adoption in the learning process among vocational students,
in addition to focusing on the usefulness and ease of use of the VL application, student
attitudes towards the use of the VL application used need to be considered. By provid-
ing a VL application that positively affects students' attitudes towards VL, vocational
students will have a higher intention to use it.
Factors affecting vocational students' intentions to use a virtual laboratory. The
results of the analysis show that there are three main factors influencing the intention
of vocational students to use VL in the learning process, namely PU, PEU, and A. At
the same time, A also acts as an intervening variable that mediates the influence of PU
and PEU on BI, making it stronger. The strength of A's influence on BI is also influ-
enced by PU and PEU. The study found that perceived usefulness, perceived ease of
use, and attitudes towards technology use significantly influenced the behavioral inten-
tion of vocational students in the Industrial Electrical Engineering study program to use
VL. This is consistent with previous studies that also showed a positive influence of
perceived usefulness, perceived ease of use, and attitudes toward technology use on
behavioral intention [22], [45], [48]. To increase the adoption of virtual laboratories
among vocational students, it is essential to focus on perceived benefits, ease of use,
and students' attitudes toward the VL application to be used [20], [22]. By providing
VL applications that are perceived as useful, perceived as easy to use, and make stu-
dents' attitudes more positive towards them, students' intentions to use the VL will be
higher [36], [46], [49]. These findings may have implications for the development and
implementation of VL applications to optimize the implementation of practical learning
in vocational education. Additionally, they can also help lecturers and educational in-
stitutions determine the appropriate VL application to be used in the learning process
as an effort to optimize learning implementation when a real laboratory is unavailable
and a remote learning system is implemented.
5 Conclusion
The study aims to investigate the factors influencing students' intention to use a VL
in remote learning practicum activities. It examines the factors that affect vocational
students' intention to use a VL in their learning process. The study identifies three main
factors: Perceived Usefulness (PU), Perceived Ease of Use (PEU), and Attitudes toward
the Use of Technology (A). The results indicate a significant and positive influence on
(1) the direct effect of PU on BI, (2) the direct effect of PEU on BI, (3) the direct effect
of A on BI, (4) the direct effect of PU on A, (5) the direct effect of PEU on A, (6) the
direct effect of PEU on PU, (7) the indirect effect of PU on BI, through A as an inter-
vening variable, and (8) the indirect effect of PEU on BI, through A as an intervening
variable. These findings indicate that PU, PEU, and A have a significant and positive
impact on vocational students' intention to use a VL for learning. To increase the adop-
tion of a VL in vocational education, it is crucial to focus on the perceived benefits,
ease of use, and attitudes of students toward the VL application. By providing a VL
106
http://www.i-jim.org
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
application that is considered useful, and user-friendly, and improving students' atti-
tudes toward a VL, the intention to use a VL among students will increase. These find-
ings have implications for the development and implementation of VL technology in
vocational education, and they can assist lecturers and educational institutions in deter-
mining appropriate VL applications for use in the learning process.
The study focuses solely on the factors that influence the intention of vocational
students to use a VL, which includes PU, PEU, and A Other factors such as social
influence, perceived risk, and trust were not considered in this study. Future research
could examine the effect of these factors on vocational students' intention to use a VL.
Additionally, this research only pertains to vocational students in the Industrial Electri-
cal Engineering Study Program. Therefore, caution should be taken when generalizing
the findings to other vocational programs.
Future studies could expand on the current research by examining the impact of other
factors, such as social influence, perceived risk, and trust, on vocational students' in-
tention to use VL. Moreover, this study could be broadened to other vocational pro-
grams to determine the generalizability of findings across different vocational pro-
grams. Lastly, future research could explore the factors that influence students' inten-
tions to use VL by utilizing other models of perceived technology.
6 References
[1] L. Liu, Y. Ling, Q. Gao, and Q. Fu, “Supporting students’ inquiry in accurate precipitation
titration conditions with a virtual laboratory tool as learning scaffold,” Education for Chem-
ical Engineers, vol. 38, pp. 78–85, Jan. 2022. https://doi.org/10.1016/J.ECE.2021.11.001
[2] D. T. P. Yanto, M. Kabatiah, H. Zaswita, N. Jalinus, and R. Refdinal, “Virtual Laboratory
as A New Educational Trend Post Covid-19: An Effectiveness Study,” Mimbar Ilmu, vol.
27, no. 3, 2022. https://doi.org/10.23887/mi.v27i3.53996
[3] S. Papadakis and M. Kalogiannakis, STEM, Robotics, Mobile Apps in Early Childhood and
Primary Education: Technology to Promote Teaching and Learning. Lecture Notes in Edu-
cational Technology.
[4] A. A. Peregudin et al., “Virtual Laboratory for Game-Based Control Systems Education*,”
IFAC-PapersOnLine, vol. 55, no. 17, pp. 344–349, Jan. 2022. https://doi.org/10.1016/
J.IFACOL.2022.09.303.
[5] X. Xie and X. Guo, “Influencing Factors of Virtual Simulation Experiment Teaching Effect
Based on SEM,” International Journal of Emerging Technologies in Learning, vol. 17, no.
18, pp. 89–102, 2022. https://doi.org/10.3991/ijet.v17i18.34489
[6] F. J. M. Veiga and A. M. V. de Andrade, “Critical Success Factors in Accepting Technology
in the Classroom,” International Journal of Emerging Technologies in Learning, vol. 16,
no. 18, pp. 4–22, 2021. https://doi.org/10.3991/ijet.v16i18.23159
[7] A.-I. Zourmpakis, S. Papadakis, and M. Kalogiannakis, “Education of preschool and ele-
mentary teachers on the use of adaptive gamification in science education,” International
Journal of Technology Enhanced Learning, vol. 14, no. 1, pp. 1–16, 2022.
[8] J. K. Pringle et al., “Extended reality (XR) virtual practical and educational eGaming to
provide effective immersive environments for learning and teaching in forensic science,”
Science & Justice, Apr. 2022. https://doi.org/10.1016/J.SCIJUS.2022.04.004
iJIM ‒ Vol. 17, No. 12, 2023
107
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
[9] J. Ramírez et al., “A virtual laboratory to support chemical reaction engineering courses
using real-life problems and industrial software,” Education for Chemical Engineers, vol.
33, pp. 36–44, Oct. 2020. https://doi.org/10.1016/J.ECE.2020.07.002
[10] S. M. E. Sepasgozar, “Immersive on-the-job training module development and modeling
users’ behavior using parametric multi-group analysis: A modified educational technology
acceptance model,” Technol Soc, vol. 68, p. 101921, Feb. 2022. https://doi.org/10.1016/
J.TECHSOC.2022.101921
[11] R. Morales-Menendez, R. A. Ramírez-Mendoza, and A. J. V. Guevara, “Virtual/Remote
Labs for Automation Teaching: A Cost Effective Approach,” IFAC-PapersOnLine, vol. 52,
no. 9, pp. 306–311, 2019. https://doi.org/10.1016/j.ifacol.2019.08.219
[12] P. Chan, T. Van Gerven, J.-L. Dubois, and K. Bernaerts, “Virtual chemical laboratories: A
systematic literature review of research, technologies and instructional design,” Computers
and Education Open, vol. 2, p. 100053, Dec. 2021. https://doi.org/10.1016/J.CAEO.2021.
100053
[13] K. Högberg, “Learning to Lead from a Distance: Reflexive Learning during a Pandemic,”
International Journal of Advanced Corporate Learning (iJAC), vol. 15, no. 1, pp. 6–19,
Mar. 2022. https://doi.org/10.3991/ijac.v15i1.27643
[14] S. Vehabi, C. S. Zakirova, A. R. Masalimova, E. R. Khairullina, E. V. Slepneva, and S. A.
Reznichenko, “Investigation of the Effects of University Field Courses on Distance Educa-
tion,” International Journal of Engineering Pedagogy, vol. 12, no. 2, pp. 20–34, 2022.
https://doi.org/10.3991/IJEP.V12I2.29333
[15] K. Folk, M. Marquart, M. Backus Florio, and K. Garay, “Developing Technical Expertise
to Support Synchronous Online Classes: The Columbia University School of Social Work
Institute on Technical Skills for Online Event Production,” International Journal of Ad-
vanced Corporate Learning (iJAC), vol. 14, no. 1, p. 15, Jun. 2021. https://doi.org/
10.3991/ijac.v14i1.19873
[16] M. L. Santos and M. Prudente, “Effectiveness of virtual laboratories in science education:
A meta-analysis,” International Journal of Information and Education Technology, vol. 12,
no. 2, pp. 150–156, Feb. 2022. https://doi.org/10.18178/ijiet.2022.12.2.1598
[17] R. Yirci, T. Karakose, I. Kocabas, T. Tülübaş, and S. Papadakis, “A Bibliometric Review of
the Knowledge Base on Mentoring for the Professional Development of School Adminis-
trators,” Sustainability (Switzerland), vol. 15, no. 4, Feb. 2023. https://doi.org/10.3390/
su15043027
[18] C. Kowald and B. Bruns, “Chatbot Maxi: A Virtual Certification Trainer in a Blended-
Learning Concept,” International Journal of Advanced Corporate Learning (iJAC), vol. 15,
no. 2, pp. 34–40, Nov. 2022. https://doi.org/10.3991/ijac.v15i2.34081
[19] H. Çivril and A. E. Özkul, “Investigation of the Factors Affecting Open and Distance Edu-
cation Learners’ Intentions to Use a Virtual Laboratory,” International Review of Research
in Open and Distributed Learning, vol. 22, 2021. http://www.jstor.org/stable/2632151
[20] A. Torres-Freyermuth, G. Medellín, G. U. Martín, and J. A. Puleo, “A virtual laboratory for
conducting ‘hands-on’ experiments on water wave mechanics,” Cont Shelf Res, vol. 243, p.
104760, Jul. 2022. https://doi.org/10.1016/J.CSR.2022.104760
[21] S. Acharya and M. Mekker, “Public acceptance of connected vehicles: An extension of the
technology acceptance model,” Transp Res Part F Traffic Psychol Behav, vol. 88, pp. 54–
68, Jul. 2022. https://doi.org/10.1016/J.TRF.2022.05.002
[22] C. Antonietti, A. Cattaneo, and F. Amenduni, “Can teachers’ digital competence influence
technology acceptance in vocational education?,” Comput Human Behav, vol. 132, p.
107266, Jul. 2022. https://doi.org/10.1016/J.CHB.2022.107266
108
http://www.i-jim.org
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
[23] A. A. P. Cattaneo, C. Antonietti, and M. Rauseo, “How digitalised are vocational teachers?
Assessing digital competence in vocational education and looking at its underlying factors,”
Comput Educ, vol. 176, p. 104358, Jan. 2022. https://doi.org/10.1016/J.COMPEDU.2021.
104358
[24] J. Aguirre, “Long-term effects of grants and loans for vocational education,” J Public Econ,
vol. 204, p. 104539, Dec. 2021. https://doi.org/10.1016/J.JPUBECO.2021.104539
[25] J. P. Ollikainen and H. Karhunen, “A tale of two trade-offs: Effects of opening pathways
from vocational to higher education,” Econ Lett, vol. 205, p. 109945, Aug. 2021.
https://doi.org/10.1016/J.ECONLET.2021.109945
[26] O. Candra, A. Putra, S. Islami, D. T. P. Yanto, R. Revina, and R. Yolanda, “Work Willing-
ness of VHS Students at Post-Industrial Placement,” TEM Journal, vol. 12, no. 1, pp. 265–
274, Feb. 2023. https://doi.org/10.18421/TEM121-33
[27] N. Zhou, D. E. H. Tigelaar, and W. Admiraal, “Vocational teachers’ professional learning:
A systematic literature review of the past decade,” Teach Teach Educ, vol. 119, p. 103856,
Nov. 2022. https://doi.org/10.1016/J.TATE.2022.103856
[28] M. Koskenranta, H. Kuivila, M. Männistö, M. Kääriäinen, and K. Mikkonen, “Collegiality
among social- and health care educators in higher education or vocational institutions: A
mixed-method systematic review,” Nurse Educ Today, vol. 114, p. 105389, Jul. 2022.
https://doi.org/10.1016/J.NEDT.2022.105389
[29] T. Karakose, I. Kocabas, R. Yirci, S. Papadakis, T. Y. Ozdemir, and M. Demirkol, “The
Development and Evolution of Digital Leadership: A Bibliometric Mapping Approach-
Based Study,” Sustainability (Switzerland), vol. 14, no. 23, Dec. 2022. https://doi.org/
10.3390/su142316171
[30] S. N. Junaini, A. A. Kamal, A. H. Hashim, N. M. Shaipullah, and L. Truna, “Augmented
and Virtual Reality Games for Occupational Safety and Health Training: A Systematic Re-
view and Prospects for the Post-Pandemic Era,” International journal of online and biomed-
ical engineering, vol. 18, no. 10, pp. 43–63, 2022. https://doi.org/10.3991/ijoe.v18i10.30879
[31] U. M. Ibrahem et al., “Interaction between cognitive styles and genders when using virtual
laboratories and its influence on students of health college’s laboratory skills and cognitive
load during the Corona pandemic,” Heliyon, vol. 8, no. 4, p. e09213, Apr. 2022.
https://doi.org/10.1016/J.HELIYON.2022.E09213
[32] C. Peechapol, “Investigating the Effect of Virtual Laboratory Simulation in Chemistry on
Learning Achievement, Self-efficacy, and Learning Experience,” International Journal of
Emerging Technologies in Learning, vol. 16, no. 20, pp. 196–207, 2021. https://doi.org/
10.3991/ijet.v16i20.23561
[33] K. Boettcher, A. Behr, and C. Terkowsky, “Visualizing Arbitrary Data in Virtual Reality,”
International journal of online and biomedical engineering, vol. 18, no. 14, pp. 114–132,
2022. https://doi.org/10.3991/ijoe.v18i14.35099
[34] P. Beňo, M. Matejdes, F. Schauer, and S. Šprinková, “REMLABNET - Solar System, New
Virtual Laboratory with Mathematical Model Using Vector Functions for Primary, Second-
ary and High Schools and Universities,” International journal of online and biomedical en-
gineering, vol. 18, no. 5, pp. 6–17, 2022. https://doi.org/10.3991/ijoe.v18i05.27935
[35] D. Karahoca, Z. F. Zaripova, A. R. Bayanova, L. S. Chikileva, S. V. Lyalyaev, and X.
Baoyun, “During the Covid-19 Pandemic, Students’ Opinions on Distance Education in De-
partment of Engineering,” International Journal of Engineering Pedagogy, vol. 12, no. 2,
pp. 4–19, 2022. https://doi.org/10.3991/IJEP.V12I2.29321
[36] E. Attié and L. Meyer-Waarden, “The acceptance and usage of smart connected objects ac-
cording to adoption stages: an enhanced technology acceptance model integrating the diffu-
sion of innovation, uses and gratification and privacy calculus theories,” Technol Forecast
iJIM ‒ Vol. 17, No. 12, 2023
109
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
Soc Change, vol. 176, p. 121485, Mar. 2022. https://doi.org/10.1016/J.TECHFORE.2022.
121485
[37] J. A. Ortiz-Sánchez, J. M. Ramírez-Hurtado, and I. Contreras, “An integrated model of struc-
tural equations with cognitive and environmental factors for the study of active commuting,”
J Transp Health, vol. 24, p. 101319, Mar. 2022. https://doi.org/10.1016/J.JTH.2021.101319
[38] J. D. Casey et al., “Use of pragmatic and explanatory trial designs in acute care research:
lessons from COVID-19,” Lancet Respir Med, vol. 10, no. 7, pp. 700–714, Jul. 2022.
https://doi.org/10.1016/S2213-2600(22)00044-3
[39] J. Fortier et al., “What type of survey research questions are identified by adults as upset-
ting? A focus on child maltreatment,” Child Abuse Negl, vol. 109, p. 104764, Nov. 2020.
https://doi.org/10.1016/J.CHIABU.2020.104764
[40] R. A. Abumalloh et al., “The impact of coronavirus pandemic (COVID-19) on education:
The role of virtual and remote laboratories in education,” Technol Soc, vol. 67, p. 101728,
Nov. 2021. https://doi.org/10.1016/J.TECHSOC.2021.101728
[41] G. Dash and J. Paul, “CB-SEM vs PLS-SEM methods for research in social sciences and
technology forecasting,” Technol Forecast Soc Change, vol. 173, p. 121092, Dec. 2021.
https://doi.org/10.1016/J.TECHFORE.2021.121092
[42] J. Hair and A. Alamer, “Partial Least Squares Structural Equation Modeling (PLS-SEM) in
second language and education research: Guidelines using an applied example,” Research
Methods in Applied Linguistics, vol. 1, no. 3, p. 100027, Dec. 2022. https://doi.org/10.1016/
J.RMAL.2022.100027
[43] C. H. Dasanayaka, Y. S. Perera, and C. Abeykoon, “Investigating the effects of renewable
energy utilization towards the economic growth of Sri Lanka: A structural equation model-
ling approach,” Clean Eng Technol, vol. 6, p. 100377, Feb. 2022. https://doi.org/10.1016/
J.CLET.2021.100377
[44] J. C. Hurley, “How to apply structural equation modelling to infectious diseases concepts,”
Clinical Microbiology and Infection, vol. 28, no. 12, pp. 1567–1571, Dec. 2022.
https://doi.org/10.1016/J.CMI.2022.05.028
[45] T. Hämäläinen et al., “Usage activity, perceived usefulness, and satisfaction in a web-based
acceptance and commitment therapy program among Finnish ninth-grade adolescents,” In-
ternet Interv, vol. 25, p. 100421, Sep. 2021. https://doi.org/10.1016/J.INVENT.2021.
100421
[46] J. K. Mullins and T. P. Cronan, “Enterprise systems knowledge, beliefs, and attitude: A
model of informed technology acceptance,” Int J Inf Manage, vol. 59, p. 102348, Aug. 2021.
https://doi.org/10.1016/J.IJINFOMGT.2021.102348
[47] L. Andrade-Arenas, D. L. Nuñez, J. V. Sandoval, W. R. Perez, and E. G. Choquehuanca,
“Proposal of a Model for the Development of University Teacher Training Through Virtual
Courses,” International Journal of Engineering Pedagogy, vol. 12, no. 3, pp. 89–109, 2022.
https://doi.org/10.3991/IJEP.V12I3.29497
[48] A. Lohbeck, I. Tóth-Király, and A. J. S. Morin, “Disentangling the associations of academic
motivation with self-concept and academic achievement using the bifactor exploratory
structural equation modeling framework,” Contemp Educ Psychol, vol. 69, p. 102069, Apr.
2022. https://doi.org/10.1016/J.CEDPSYCH.2022.102069
[49] H. Hafiza Razami and R. Ibrahim, “Models and constructs to predict students’ digital edu-
cational games acceptance: A systematic literature review,” Telematics and Informatics, vol.
73, p. 101874, Sep. 2022. https://doi.org/10.1016/J.TELE.2022.101874
110
http://www.i-jim.org
Paper—Analysis of Factors Affecting Vocational Students' Intentions to Use a Virtual Laboratory…..
7 Authors
Doni Tri Putra Yanto is a doctoral student in the field of Technology and Voca-
tional Education with a concentration in electrical engineering education. He is a lec-
turer and researcher at the Faculty of Engineering, Universitas Negeri Padang, Sumatra
Barat, Indonesia. His research extensively covers areas such as technology, vocational
education and training (TVET), technology-enhanced learning (TEL), remote learning,
blended learning, learning models in TVET, and learning media in TVET (email: do-
nitriputra@ft.unp.ac.id).
Sukardi is an associate professor and researcher at the Electrical Engineering De-
partment, Faculty of Engineering, Universitas Negeri Padang, Sumatra Barat, Indone-
sia. His research interest focuses on technology, vocational education and training
(TVET), learning models, and electrical engineering education (email: sukardi-
unp@ft.unp.ac.id).
Maryatun Kabatiah is an active lecturer and researcher in the Department of Civic
Education, Faculty of Engineering, Universitas Negeri Medan, Sumatra Utara, Indone-
sia. Her research extensively covers areas such as civic education learning, Developing
Learning modules on civic education, Learning model on civic education, Value, and
Moral Education, and Educational Technology (email: maryatunkabatiah@unimed.
ac.id).
Hermi Zaswita is an active lecturer and researcher in the English Education Study
Program, at STKIP Muhammadiyah Sungai Penuh, Jambi, Indonesia. Her research in-
terest focuses on English Language Teaching (ELT), Learning Media, English for Spe-
cific Purposes (ESP), and Classroom Interaction (email: zaswitahermi@gmail.com).
Oriza Candra is an associate professor and researcher at the Electrical Engineering
Department, Faculty of Engineering, Universitas Negeri Padang, Sumatra Barat, Indo-
nesia. His research interest focuses on technology, vocational education and training
(TVET), learning models, and electrical engineering education (email: orizacan-
dra@ft.unp.ac.id).
Article submitted 2023-02-06. Resubmitted 2023-04-04. Final acceptance 2023-04-04. Final version pub-
lished as submitted by the authors.
iJIM ‒ Vol. 17, No. 12, 2023
111
