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SIDEK, S.F., YATIM, M.H.M., SAID, C.S., The Design, and Validation of a tool to Measure Content Validity of a
Computational Thinking Game-Based Learning Module for Tertiary Educational Students, pp. 1-9 1
IOER INTERNATIONAL MULTIDISCIPLINARY RESEARCH JOURNAL, VOL. 4, NO. 1, MARCH 2022
THE DESIGN AND VALIDATION OF A TOOL TO MEASURE CONTENT
VALIDITY OF A COMPUTATIONAL THINKING GAME-BASED LEARNING
MODULE FOR TERTIARY EDUCATIONAL STUDENTS
SALMAN FIRDAUS SIDEK1, MAIZATUL HAYATI MOHAMAD YATIM2*, CHE SOH SAID3
https://orcid.org/0000-0001-5412-36531, https://orcid.org/0000-0003-4504-27252
https://orcid.org/0000-0002-2819-42953
salmanfirdaus@fskik.upsi.edu.my1, maizatul@fskik.upsi.edu.my2
chesoh@fskik.upsi.edu.my3
Computing Department, Faculty of Art, Computing and Creative Industry
Education University of Sultan Idris, Malaysia1-3
ABSTRACT
This study proved the design and content validity process of a computational thinking game-based
learning module. The process involved a two-step method: instrument design and judgmental evidence.
Content domain identification, item generation, and instrument construction were included in the former
step while the latter involved seven experts to review and rate the essentiality, relevancy, and clarity of
the generated 30 items in the first and 34 items in the second round. Suggestions and ratings by the panel
of experts in the second step were used to examine the instrument content validity through content validity
ratio (CVR), content validity index (CVI), and modified kappa statistic approach. The findings manifested
the second round promised better results with the increment of totally essential items by 59.41 percent
and the increment of total relevant, clear, and excellent items by 3.33 percent. It implies in the second
round that 79.41 percent of overall items were significantly essential, and 100 percent of the overall items
were significantly relevant, clear, and excellent. Overall, the instrument got significant content validity after
the second round by s-CVI/UA=0.97 and s-CVI/Average=0.99. Hence, the instrument has a great potential
to measure the content validity of a brand-new computational thinking game-based learning module.
However, it was then recommended to involve more experts during content domain determination and
item generation and to further explore the findings that support the content validity of 33 items on
instrument reliability.
Keywords: content validity ratio; content validity index; instrument; learning module; modified kappa
statistic
INTRODUCTION
Generally, the validity process is a series of
actions taken to determine the accuracy of the
instrument used as a measurement tool measuring
the concept that it should measure. In the context
of module development, a measurement tool
should be able to measure accurately and
systematically the content of the module (Noah &
Ahmad, 2005). In any research field, the validity
process is crucial since it presents the ability of the
instrument to meet the purpose of the study (Kipli
& Khairani, 2020). It is exclusive for a specific
purpose on a special group of respondents
(Zamanzadeh et al., 2015). Therefore, the
evidence should be obtained on the study that
used the instrument (Waltz et al., 2010). There are
various types of validities but only quadruplet
synonyms with educational research; (1) content,
(2) construct, (3) face, and (4) criterion-related
validity (Oluwatayo, 2012).
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SIDEK, S.F., YATIM, M.H.M., SAID, C.S., The Design, and Validation of a tool to Measure Content Validity of a
Computational Thinking Game-Based Learning Module for Tertiary Educational Students, pp. 1-9 34
IOER INTERNATIONAL MULTIDISCIPLINARY RESEARCH JOURNAL, VOL. 4, NO. 1, MARCH 2022
During instrument development, content validity is
prioritized because it is a prerequisite for other
validities (Zamanzadeh et al., 2015). It helps to
improve the instrument through recommendations
from the experts’ panel and the information related
to representativeness and clarity of the items (Polit
& Beck, 2006). Content validity indicates the extent
to which items in an instrument adequately
represent the content domain and in turn allows the
reliability of the instrument to be determined
(Zamanzadeh et al., 2015).
An instrument that has good validity
ensures the high reliability of the obtained result.
Therefore, content validity is crucial to preserve the
strength of the study design. Content Validity Index
(CVI) and Content Validity Ratio (CVR) are the
most widespread approaches used to measure the
content validity of a measurement tool
quantitatively (Kipli & Khairani, 2020; Rodrigues et
al., 2017). Although it was invented through
educational studies by an educational specialist
(Polit & Beck, 2006), however Kipli and Khairani
(2020) claimed only a few educational studies have
been found applying this approach, but the list of
human resources, nursing, and health studies
continue to grow.
Ironically, the current situation shows a
promising trend of the CVI approach in educational
studies. CVI approach was well implemented in the
process of validating the content of the new
educational module or in the process of validating
the content of the instrument used to evaluate
programs and training (Kipli & Khairani, 2020;
Mensan et al., 2020). But the lack of concern to use
a valid instrument to measure the content validity
of a brand-new module led to the purpose of this
study. Hence, the purpose of this study was to
examine the content validity of the instrument
which was adopted to measure the content validity
of a brand-new computational thinking game-
based learning module developed for tertiary
educational students. This was echoing
Zamanzadeh et al. (2015) and Waltz et al. (2010)
where the validity process was exclusive for a
specific purpose on a special group of respondents
so the evidence should be obtained on the study
that used the instrument.
OBJECTIVE OF THE STUDY
This study aimed to prove the design and
validation of an adapted research instrument for
measuring the content validity of a brand-new
learning module through CVR and CVI
approaches.
MATERIALS AND METHODS
The process of designing the research
instrument was done through a ‘three-step
process’ namely (1) identifying content domains,
(2) content sampling/item generation, and (3)
instrument construction (Nunnally & Bernstein,
1994). It was the first step of instrument
development as described by Armstrong et al.
(2005) and Stein et al. (2007) via the two-step
method. While the second step involved a judging
process conducted with a panel of experts from
various and related academic backgrounds and
research expertise. Their confirmation indicated
the instrument items and the entire instrument had
content validity.
First step: Instrument design Identifying
content domain
Content domain refers to the content area
associated with measured variables (Beck &
Gable, 2001). The expected content domain for the
instrument, in general, revolved around
computational thinking (CT) in education.
However, there was little knowledge about the CT
term as it was arguably relatively new in Malaysia.
Therefore, an extensive literature search was
needed to determine the content domain for this
study (Dongare et al., 2019). Four major online
repositories were selected over 13 other online
repositories in conjunction with their wide usage in
the studies related to CT skills (Sidek et al., 2020).
An extensive literature search through these
leading online repositories was carried out that
yielded 116 articles that met selection criteria.
Item generation. The instrument was
adapted from the instrument produced by Ahmad
(2002). This instrument was developed to test the
2
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SIDEK, S.F., YATIM, M.H.M., SAID, C.S., The Design, and Validation of a tool to Measure Content Validity of a
Computational Thinking Game-Based Learning Module for Tertiary Educational Students, pp. 1-9 35
IOER INTERNATIONAL MULTIDISCIPLINARY RESEARCH JOURNAL, VOL. 4, NO. 1, MARCH 2022
content validity of a teaching, motivation, training,
or academic module. It contained five items with a
five-point Likert scale and was built based on
Russell’s view on the condition of module validity
(Russell, 1973; Russell & Lube, 1974). In this
study, these items were discussed and reviewed
by an expert (Torkian et al., 2020), with more than
12 years of experience in educational
measurement and evaluation. The discussion
revolved in a context of content domain identified
through an extensive literature review which was
focused on the CT in education.
Instrument construction. The
construction of the instrument involved refining and
arranging all generated items into appropriate
format and arrangement so that the final items will
be collected in a usable form (Lynn, 1986).
Second step: The judging process
During this step, the validity of the
instrument items and the entire instrument will be
determined (Zamanzadeh et al., 2015). For this
purpose, two approaches namely CVR and CVI
were performed. The CVR was an approach used
to maintain confidence in selecting the most
important and correct content in the instrument
(Zamanzadeh et al., 2015). Therefore, the experts’
panel were asked to provide scores on the
essentiality of each item in the instrument based on
a 3-point Likert scale: 1 for ‘not necessary’, 2 for
‘useful but not essential, and 3 for ‘essential’
(Zamanzadeh et al., 2015). The CVR score ranges
between 1 and -1 and a higher score indicated
greater agreement among experts regarding the
essentiality of an item in the instrument (Rodrigues
et al., 2017). The CVR was calculated using (Ne-
N/2)/(N/2), where Ne was the number of experts
who denoted an item as ‘essential’ and N
represents the total of experts (Zamanzadeh et al.,
2015). For the study, items in the instrument with
an acceptable level of significance of 0.99 and
above were remained because the minimum
number of experts involved in scoring, N, was set
to five (Lawshe, 1975). CVI was divided into two
types namely item-wise content validity index (i-
CVI) and scale-wise content validity index (s-CVI)
(Zamanzadeh et al., 2015). i-CVI represented the
proportion of agreement regarding the
relevance or clarity of each item and its value was
in the range of 0 to 1 (Lynn, 1986). It was calculated
based on the number of experts who gave a score
of 3 or 4 for each item divided by the total of experts
(Asun et al., 2015). i-CVI>0.79 indicated an item
was relevant or clear (Rodrigues et al., 2017).
0.70<=i-CVI<=0.79 indicated an item needed
revision while i-CVI<0.70 indicated an item can be
removed from the instrument (Zamanzadeh et al.,
2015; Rodrigues et al., 2017). Meanwhile, s-CVI
was the proportion of items in the instrument which
were rated 3 or 4 by the experts (Beck & Gable,
2001). There were two methods used to calculate
s-CVI namely universal agreement among experts
(s-CVI/UA) and the mean of i-CVI (s-CVI/Average).
s-CVI/UA was calculated by dividing the number of
items with a relevance-related i-CVI score equal to
1 by the total of items in the instrument. Before
determining s-CVI/UA, the scale should be first
converted into a dichotomous scale that combined
scales 1 and 2 as irrelevant or 0 while scales 3 and
4 were combined as relevant or 1 (Lynn, 1986).
While s-CVI/Average was calculated by dividing
the total relevance-related i-CVI by the total of
items in the instrument (Zamanzadeh et al., 2015).
The best content validity for the whole instrument
was obtained by s-CVI/UA>=0.8 and s-
CVI/Average>=0.9 (Shi et al., 2012; Rodrigues et
al., 2017). For this study, a panel of experts was
appointed to provide scores related to the
relevancy and clarity of each item based on a 4-
point Likert scale (Davis, 1992). The scale was
added to the evaluation sheet to guide experts for
the scoring method.
Nevertheless, the method of measuring the
content validity of an instrument through CVI
ignored the probability of inflated values caused by
chance agreement. Therefore, the CVI method
was implemented jointly with Kappa statistics to
provide information on the degree of agreement
beyond chance (Wynd et al., 2003). In this
scenario, the index of agreement between experts
was adjusted according to the chance agreement
(Polit et al., 2007).
The probability of chance agreement for
each item must first be calculated using the
following formula, Pc=[N!/{A!(N-A)!}]*0.5N, where N
was the total of experts and A was the number of
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SIDEK, S.F., YATIM, M.H.M., SAID, C.S., The Design, and Validation of a tool to Measure Content Validity of a
Computational Thinking Game-Based Learning Module for Tertiary Educational Students, pp. 1-9 36
IOER INTERNATIONAL MULTIDISCIPLINARY RESEARCH JOURNAL, VOL. 4, NO. 1, MARCH 2022
experts who agreed the item was relevant or 1
(Rodrigues et al., 2017). Next, the value of Kappa,
K was calculated using the formula K=(I-CVI-
Pc)/(1- Pc). If Kappa>0.74, it indicated the item was
‘excellent’. A 0.60<=Kappa<=0.74 indicated the
item was ‘good’ while 0.40<=Kappa<=0.59
indicated the item was ‘moderate’ (Rodrigues et al.,
2017). Therefore, the involvement of seven
experts (n=7) appointed as the panel was required
to provide scores to determine the CVR and CVI
for each item in this study. The expected minimum
response was (n=5) or 71%. It was based on the
recommendation by Armstrong et al. (2005) where
the appropriate number of raters ranges between
two to 20 people. Moreover, this number was also
equal to the minimum number (n=5) that can
provide adequate control over the chance
agreement (Rodrigues et al., 2017). The experts
appointed as the panel in this study have extensive
academic background, expertise, and research
experience in the related fields around five to 28
years.
After determining the experts’ panel,
quantitative data began to be collected from
several aspects such as relevancy, clarity, and
essentiality of each item. The purpose was to
measure the constructs operationally defined by
the items and the aim was to obtain content validity
for the instrument (Rodrigues et al., 2017). For this
study, quantitative data were collected in two
rounds, aimed at increasing confidence in the
findings.
Therefore, several documents have been
attached along with the evaluation sheet before
being submitted to the experts’ panel via email.
The attached document contains an agreement
sheet and instructions on how to provide a score
for each item. To assess whether the items were
relevant, clear, and essential, the panel were given
a set of summarized Q-bot module and evaluation
sheet containing four matters namely (1) the
relevancy of each item in the instrument, (2) the
clarity, i.e. in term of words used, (3) the
essentiality, i.e. how necessary the item be
included in the instrument, and (4) the column for
suggestions of improvement for each item and
overall instrument. The evaluation sheet also
contained 3-point and 4-point Likert scales that can
guide the experts while providing a score on
each item (Zamanzadeh et al., 2015).
RESULTS AND DISCUSSION
1. First step
During the initial stage, the instrument
contained only five items adapted from the
instrument produced by Ahmad (2002). After the
discussion and review by an expert in the field of
educational measurement and evaluation with
more than 12 years of experience, the items were
found to be too general. Therefore, an extensive
literature review of 116 documents that focused on
CT in education has been done. Five focused
research areas were found: 1) definition and
concept, 2) curriculum, 3) pedagogy, 4) teaching
and learning, and 5) assessment. Various features,
concepts, or elements connected to the skills of CT
were also discovered. It was often found to differ
according to the tools, target groups, curriculum, or
pedagogy implemented to cultivate those skills
(Sidek et al., 2020). Nevertheless, there were four
features or elements of computational thinking
skills (CTSEs) that have often been focused on the
tertiary level from a total of 66 CTSEs found (Sidek
et al., 2020). These four elements known as (1)
abstraction, (2) algorithm, (3) decomposition, and
(4) generalization was seem to remain relevant at
the tertiary level due to the consensus of its
definition that has been reached among
researchers (Sidek et al., 2020).
In addition, the extensive literature review
conducted also found the effectiveness of game-
based learning (GBL) from various perspectives.
Practically, GBL can be implemented through three
approaches. According to Pellas and Vosinakis
(2017), only a few studies have been done in the
playing games approach. Therefore, the study
found the playing games approach as an
opportunity to be explored in the learning of CT
skills. The process of gamification allowed non-
game activities to be converted into playing
activities by applying game elements (Kotini &
Tzelepi, 2015). Through the extensive literature
review conducted, a total of 31 game elements
were found. However, five elements were often
4
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SIDEK, S.F., YATIM, M.H.M., SAID, C.S., The Design, and Validation of a tool to Measure Content Validity of a
Computational Thinking Game-Based Learning Module for Tertiary Educational Students, pp. 1-9 37
IOER INTERNATIONAL MULTIDISCIPLINARY RESEARCH JOURNAL, VOL. 4, NO. 1, MARCH 2022
used and straight away adapted in this study
namely (1) storytelling, (2) goals, (3) rules, (4)
feedback, and (5) rewards (Sidek et al., 2020).
As a result, the original five items were
made into constructs and broken down into 30
other items based on the content domain identified
through an extensive literature review that
specifically revolved around teaching and learning
of CT skills. The 30 items were framed based on
five constructs: 1) target population, 2) module
content, 3) method and duration of delivery, 4)
student achievement and 5) student attitude. All
generated 30 items were then refined and
arranged into appropriate format and arrangement
so that the final items were collected in a usable
form.
2. Second step
At this stage, a total of seven expert panels
(n=7) were appointed to evaluate the instrument.
The expected response from seven experts was
set to 71 percent (n=5) because the minimum
value of CVR=0.99 needed five experts (n=5) to be
involved. For the study, the response during the
first round was 85.71 percent (n=6) and 71.43
percent (n=5) for the second round. Both have met
the expectation.
First-round
For this study, items were categorized as
essential and will be remained if CVR>=0.99. This
value was considered because the number of
experts involved in providing scores during the first
round was six (n=6) (Lawshe, 1975). In the first
round of judgment, only 6 items in the instrument
had CVR>=0.99 while 24 items had CVR<0.99.
This finding indicated only 20 percent of 30 items
were significantly essential and will be remained.
However, items with CVR<0.99 were also
remained (Rodrigues et al., 2017), due to several
factors such as most of the items were relevant and
clear (i-CVI>0.79) as well as excellent
(Kappa>0.74).
Meanwhile for the i-CVI-relevancy scores,
29 items or 96.67 v had i-CVI>0.79 but 1 item or
3.33 percent had i-CVI<0.70. The finding indicated
most of the items were relevant (i-CVI>0.79)
except one item from Section A: Target population
(item 1.3) that could be considered for exclusion
from the instrument because of irrelevance (i-
CVI<0.70). Furthermore, the i-CVI-clarity scores
during the first round showed 29 items or 96.67
percent had i-CVI>0.79 while 1 item or 3.33
percent had i-CVI<0.70. This finding indicated
most of the items were clear (i-CVI>0.79) except
one item from Section B: Content of Module (item
2.10) that could be considered to be removed from
the instrument (i-CVI<0.70). Next, the finding of the
modified Kappa statistic, K=(I-CVI- Pc)/(1- Pc) in the
first round of judgment showed 29 items or 96.67
percent had Kappa>0.74 while 1 item or 3.33
percent had Kappa<0.6. This finding indicated
most of the items were excellent except one item
from Section A: Target Population (item 1.3) which
was moderate (0.40<=Kappa<=0.59). Overall, it
was found the whole instrument with 30 items had
the best content validity (s-CVI/UA>=0.8, s-
CVI/Average>=0.9) where s-CVI/UA=0.9667 and
s-CVI/Average=0.9889 in detail.
Even though the whole instrument
generally enjoyed the best content validity in the
first round of judgment, the action was still taken on
each item based on the CVR, i-CVI, Kappa, and
suggestions from expert panels. It was intended to
increase confidence in the selection of appropriate
items in the instrument to measure what should be
measured. Table 1 shows the items with
problematic scores after the first round.
Table 1
The items with problematic scores in the first round
Based on Table 1, item 1.3 which related to
Section A: Target Population was found non-
essential (CVR<0.99), irrelevant (i-CVI<0.70) and
moderate (0.40<=Kappa<=0.59). While the
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SIDEK, S.F., YATIM, M.H.M., SAID, C.S., The Design, and Validation of a tool to Measure Content Validity of a
Computational Thinking Game-Based Learning Module for Tertiary Educational Students, pp. 1-9 38
IOER INTERNATIONAL MULTIDISCIPLINARY RESEARCH JOURNAL, VOL. 4, NO. 1, MARCH 2022
comments from the experts’ panel were as
followed:
P4: Not necessary as this module has no
gender bias.
P5: Is there any difference in the ways the
items in the module will be perceived by boys and
girls (bias)?
Added to this, item 1.3 was removed from
the instrument. Even though only six items related
to Section B: Content of Module classified as
essential (CVR >= 0.99), the balance of 23 items
with i-CVI<0.99 were remained (Rodrigues et al.,
2017). This was based on the justification that all
the items were relevant (i-CVI>0.79) and excellent
(Kappa>0.74), and fundamental to obtain content
validity of the module as it involved constructs
adapted from the original instrument by Ahmad
(2002). Accordingly, due to the findings and
suggestions from the experts’ panel, the items
were broken down into several items, modified,
combined, or remained. The improvement caused
the increment of the number of items from 30 to 34.
Table 2 summarized the series of actions
performed and the total number of recent items.
Table 2
The number of recent items after improvement
Second-round
The improved 34 items were trained for the
second round of the judgment. Items were
categorized as essential and will be remained if
CVR>=0.99, and this was according to the
number of experts involved in providing the scores
(n=5) (Lawshe, 1975). Based on the findings in this
round, the percentage of items categorized as
insignificant had begun to decline. This was shown
when out of 34 items, 27 items or 79.41 percent
had CVR>=0.99 while 7 items or 20.59 percent had
CVR<0.99. As compared to the former round, the
latter attain better findings as items categorized as
essential increased by 59.41 percent. However,
item 3.2 which related to Section C: Method and
Duration of Module Content Delivery was absorbed
into items 3.1 and 3.3 since the CVR was too low
and the suggestion from the expert was as
followed:
P4: It is recommended to be stated in a
form of a short course (by day) or a long course (by
week).
While the findings of i-CVI-relevancy and i-
CVI-clarity of each item proved that all 34 items
had i-CVI>0.79. It indicates that 100 percent of the
items were significantly relevant and clear and 3.33
percent increment of relevant and clearer items as
compared to the first round. Furthermore, the
second round also marked positive vibes for
modified Kappa statistic, K, where all 34 items had
Kappa>0.74. The satisfied findings marked 3.33
percent of increment and contribute to 100 percent
of the items were excellent. The number of items
with CVR, i-CVI, and Kappa scores obtained in the
second round is summarized via Table 3.
Table 3
The number of items according to its interpretation
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Regarding the CVR, i-CVI, and Kappa in
the second round, the promising result of the
overall content validity of the instrument was
expected. There is an increment of 0.0039 percent
and 0.0052 percent for the s-CVI/UA and s-
CVI/Average. Therefore, the s-CVI/UA>=0.9706
and s-CVI/Average>=0.9941 denotes that the
instrument got the best content validity (s-
CVI/UA>=0.8, s-CVI/Average>=0.9) with the
finalized 33 items overall.
CONCLUSIONS
Content validity is a prerequisite for other
validities and helps in preparing the instrument for
reliability evaluation. The process of content
validity involved a two-step method; (1) instrument
design, and (2) judgment process. The former was
carried out through three-step process while the
latter involved a panel of seven experts (n=7). The
CVI was divided into two: i-CVI and s-CVI. The i-
CVI had been reported by most papers, but s-CVI
was vice-versa. Therefore, this study had fulfilled
this gap. Through iterative approaches, the content
validity process demonstrated preferable results
via the second round where the study revealed the
instrument obtained an appropriate level of content
validity as expected. The s-CVI/UA and s-CVI/Ave
approaches suggested the overall content validity
of the instrument was at the best (s-CVI/UA=0.97,
s-CVI/Ave=0.99). The practice on content validity
study helped students understand the accurate
approach to criticize research instruments.
Therefore, CVI is considered one of the promising
approaches for instrument development in
educational studies and effective method in
calculating the content validity of a new learning
module.
RECOMMENDATIONS
The study of content validity began with the
discussion on the instrument adapted from Ahmad
(2002). The original instrument was adapted by
detailing the items based on the content domains
determined via extensive literature review
(Dongare et al., 2019) as well as the discussion
and review by an expert (Torkian et al., 2020). In
the study, an expert with more than 12 years
of experience in educational measurement and
evaluation was involved but it was recommended
to involve more experts (Simbar et al., 2020) or
conduct focus groups accustomed with the
concept (Zamanzadeh et al., 2015) via semi-
structured interviews. It was given since the
qualitative data collected in the interview is
considered as an invaluable resource in item
generation, and it could clarify and enhance the
identified concept (Tilden et al., 1990).
Furthermore, the findings support the content
validity of 33 items should be further explored on
instrument reliability.
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AUTHORS’ PROFILES
Salman Firdaus b Sidek, M.Sc. (C.Sc.–
Information Security), University of Technology,
Malaysia; B.Sc. (Computer Science), University of
Technology, Malaysia.
Specialized in Software
Engineering and Information
Security.
AP Dr. -Ing Maizatul Hayati bt
Mohamad Yatim, Ph.D. in
Computer Science, Otto-Von-
Guericke University of Magdeburg,
Republic Germany; M.Sc.(IT),
University of North, Malaysia; B.IT.(IT), University
of North, Malaysia. Specialized in Human-
Computer Interaction (Game Usability).
Che Soh b Said, Ph.D. in
Education and Multimedia
(Computer Science), University of
Science, Malaysia; M.C.Sc.
(Computer Science), University of
Putra, Malaysia; B.C.Sc. and Edu
(IT), University of MARA, Malaysia. Specialized in
Instructional Technology.
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commons.org/licenses/by/4).
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