Scale Development To Evaluate Students’ Intention To Use Technology and E-learning In Libyan Higher Education

Tark Melud Elmalti
Lecturer, Department of Computer, Faculty of Technical Engineering, Zwara, Libya
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Abstract - This paper describes the development of an
instrument to assess student intention to use technologyande-
learning in Libyan Higher Education (LHE). Regardless the
research that has been conducted to examine the factors that
explain students’ intention to use technology and e-learning,
not many have developed an instrument to determine these
factors. Four independent variables used (computer–internet
experience, computerself-efficacy,technology-internetquality,
and attitudes toward use), intention to use technology and e-
learning used as a dependent variable. It is major to knowand
evaluate the variables that influence student intention to use
technology and e-learning. The final retained 29-item
intention to use technology and e-learning instrument was
acceptable with sample size of 273. Based on the findings, this
article proposes guidelines for further investigation by
applying statistical analysis on another sample to show the
relations between the four independent variables and the
dependent variable intention to use.
Key Words: Technology-internet quality, Computer self-
efficacy, Computer-internet experience, Attitudes toward
using, intention to use.
1.INTRODUCTION
Recently e-learning systems have been used in learning and
teaching inmany higher education institutions,that resulted
in changes in education process in those institutions [1].
Furthermore, the use of e-learning systems in universities is
an effect of progressionof IT. Accordinglythegrowthof Web
application e-learning systems are becoming an important
instructional medium in universities [2]. Additionally, with
the wide spread use of the WWW, many higher education
institutions(HEIs) are taking the opportunity to develop e-
learning courses [3]. Furthermore, with the growth of IT, e-
learning systems are becoming an integral part of teaching
and learning process in HEIs. [4]
Despite that, Learners involved in distance education are
more likely to have insecurities about the learning, self-
evaluation problems, lack of support services such as tutors
and technical assistance, feelings of isolation, and
inexperience with this mode of learning, which leads to
academic problems. [5]
when applying a learning tool or system for learners, it is
necessary to investigate both teachers’ and learners’
attitudes toward that tool or system. [6]
On the whole from our point of view, there are a number of
factors that influence individuals’ intention to use
technology and e-learning, the most critical of these factors
will be reviewed in the next section.
1.1 Factors that influence intention to use
Literature on e-learning systems in higher education has
identified a number of factors that contribute to students’
and instructors’ intention to use e-learning systems. These
factors include individuals’ CSE, CIE, ATE and TIQ .
However a choice of factors of IT acceptance have been
examined in past research, CSE is one of these factors and
has been recognized as a main key of IT-related ability and
the use of IT. [7]
Our first factor is CSE, According to Hayashi et al., The belief
in individual’s ability has an influence on choice of activities,
degree to effort expended, and persistence of effort.
Consequently, CSE exerts a significant influence on
individuals’ affecting reactions to IT,their intentiontouseIT,
and their actual use of IT [8]. Igbaria and Ivari mentioned
that, CSE include that “the individuals prefer to avoid
computers and less likely to use them” , because, they
consider computers too complex and believe that they will
never be able to control these computers.[9]
Other studies focused on CIE, Morss concluded that older
students who had more experience of the technology used a
learning management (WebCT) morethanyounger students
who had less experience [10]. As well, Kerka found that
student success in distance learning depends on technical
skills in computer process and internet navigation [11].
Abbad, et. al. concluded that, students who are frequent
and/or heavy users of the Internet are more likely to use e-
learning systems[12]. While Selim reported in his study,
Previous student experience with personal computers came
as the most critical factor in the category of student
motivation and technical compentency for e-learning
acceptance.[13]
The third factor is ATE, According to Davis et al., attitude is
the degree to which the individual is interested in specific
systems, which has a direct effect on the intention to use as
well as actual use of those systems[14]. While Venkatesh &
Brown defined the Attitude toward behavior is “a person’s
favorable/unfavorable evaluation of the behavior in
question”[15]. Whereas Ajzen confirmed that in general
accepted that attitude represents a “summaryevaluationofa
psychological object captured in such attribute dimensions
as good-bad, harmful-beneficial, pleasant-unpleasant, and
likeable-dislikeable”[16]. Selim concluded that, individuals’
behavioral intention is said to be determined by their
International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 01 | Jan 2022 www.irjet.net p-ISSN: 2395-0072
Scale Development To Evaluate Students’ Intention To Use Technology
and E-learning In Libyan Higher Education
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 375

attitude concerning the behavior - whether they feel that
performing that behavior is good or bad.[17]
Our last factor is TIQ, several researchers indicated that
technology-internet qualitysignificantlyaffectsatisfactionin
e-learning. Amoroso et al.,found that,Users will be willing to
adopt such a tool with few barriers and satisfaction will be
improved [18]. Consequently, the higher the quality and
reliability in IT, the higher the learning effects will be [19].
Several researchers indicate that technology quality and
Internet quality significantly affect satisfactionin e-Learning
[20][21]. Users will be willing to adopt such a tool with few
barriers and satisfaction will be improved[22][23].
1.2 ICT in Libya
Libyan national policy for Information and Communication
Technology (ICT) in education was launched in 2005 and
managed by the Ministry of Education and the Ministry of
Vocational Training. The government is determined to
provide tools and ICT skills on a large scale to all sectors of
the country [24]. Though one of the agents to develop the
quality of education through ICT is developing open and
distance learning as well as continued education. But
implementing of E-learning systems in Libya still in
determining years [25], the attempt to inspect e-learning
systems still as case study because of the lacking of using
ICT, i.e. using of ICT is still not widespread. According to
[26], the barriers to implement and use e-learning in Libya
includes technological barriers, that is, lack of networks and
systems infrastructures, lack of experience in using
technology; lack of appropriate internet service. In a
comparison between Libyan and African institutions, [27]
classified the challenges associated to the implementation
and using of e-learning and ICT to three categories: lack of
ICT infrastructure, lack of qualified personnel,andresistance
to change.
Based on the review of the factors that influence using
technology and e-learning covered in previous section, our
aim in this paper is to develop an instrument for student
intention to use technology and e-learning in LHE. [28]
suggested a number of rules and steps should be followed in
Scale development. These steps are as the following: (1)
Generating an item pool, (2) Determining the format for
measurement, (3) Content validity and review by experts,
(4) Administration of the items to a development sample,
(5)Analysis ofthe psychometric properties, (6) Optimization
of the scale.
Thus in this paper, we followed the sequence of steps
mentioned before in the development of the scale starting
from item pool generation to optimization of the scale to
assess student intention to use technology and e-learning in
LHE.
2. Methods
Based on the goals of the paper an students’ questionnaire
(STQ), conducted, there are some elements should be
considered when we investigating the technology and e-
learning in Libyan higher education, we assumed that,
students’ intention to use technology and e-learning in
Libyan higher education is influenced bysome factors found
against the development and progressing in this field in the
country. These factors are, computer self-efficacy (CSE),
computer-internet experience (CIE), , technology- internet
quality (TIQ) and attitudestowardtechnologyande-learning
(ATE).
2.1 Generating an item pool
In the beginning, a pool of items correlated to intention to
use e-learning and ICT was generated, sufficient review and
investigation of the existing literature, covering student
intention to use ICT and e-learning. At this phase a list of 33
items were recognized, To ensure the content validity of the
scales, a set of items selected must be representative of the
concerned domain content [29][30]. Therefore, validated
items adapted from prior studies were used to measure
computer self-efficacy, computer and internet experience,
technology-internetquality,attitudestowardtechnologyand
e-learning, and intention to use technology and e-learning
[10][11][14][15][16][31][32][34][35][36][37]. These items
reflect a latent association with concept of using ICT and e-
learning. Both positively and negatively worded statements
were included in the pool.
2.2 Determining the format of the scale
At this step, different scaling options have been reviewed.
Then, the Likert scale was chosen because of its ease of use,
common use in intention measurement, higher reliability
coefficients with less items, and methodofsummatedratings
[38]. Therefore, we used the following two scales : The first,
four-point scale to evaluate computer and internet
experience(CIE) given withthe numericalvalues assigned to
each point: (1=Never, 2=Monthly, 3=Weekly, and 4=Daily).
For the other four constructs we have used five-point scale
to evaluate : computer-internet self-efficacy(CSE),
technology-internet quality(TIQ), Attitudes toward
technology and e-learning(ATT), and intention to use
technology and e-learning(IUT) with the numerical values
assigned to each point progressive from 1 to 5.
2.3 Content Validity
Content validity is defined as the degree to which the
elements of an assessment of instrument are relevant to and
representative of the targeted construct for a particular
assessment purpose [39]. Therefore, as mentioned before,
validated items adapted from prior studies were used to
measure computer-internet experience, computer self-
efficacy, technology-internet quality, attitudes toward
technology and e-learning, and intention to use technology

and e-learning. The participants indicated their answers
with using a four-point and five-point Likert-type scale. we
measured demographic information: gender, age, field of
work, teaching experience years, and scientific grade.
2.4 Administration of the Items to a Development
Sample
A 33 items questionnaire was conducted in five constructs,
each of which contains a number of items, then, the
questionnaire was translated to Arabic language and
distributed to a sample of 273 students in LHE (Zawia
University, and institutions of the national authority for
technical education) in theacademic year2017/2018. Given
that, for scale development a large sample would reduce
subject variance [40]. [41] advice a ratio of 5 to 10 subjects
per item. [42] suggest a sample size for analysis N≥50+8M,
or N≥ 104 +M. where M is the explanatory variables. So,
distribution of the questionnaire containing 33 items to a
sample size of 273 was measured suitable. Of the 273
surveys, a 63% response rate was achieved (172 usable
responses).However this was consideredas adequateat this
instrument.
3. Data analysis
The reliability alpha coefficient for the scale with 33 items
was tested and found 0.81, which indicated that the items in
the scale were highly inter correlated and were all
measuring the sameattribute, i.e. intentiontousetechnology
and e-learning. Then we investigatedadditionaloptimization
of the instrument by examining the reliability coefficient of
each construct independently. We found that the 6-item
construct1(CIE) had a reliability coefficient of 0.79, 12-item
construct2(CSE) had a reliability coefficient of 0.89, 5-item
construct3(TIQ) had a reliability coefficient of 0.49, 7-item
construct4(ATE) had a reliability 0.90, and 3-item
construct5(IUT) had a reliability 0.74, indicating high inter-
item correlation within all these constructs. According to
[43], Cronbach’s alpha is reliable if its value is at least 0.7.
But, we were concerned in understanding how many
constructs or variables underlay the set of 33 items in the
scale. Therefore, we performed exploratory factor analysis
on the sample.
Examining factoranalysis using principalcomponents factor
extraction and VARIMAX rotation was conducted to identify
the factors in our work. Four commonly rules were applied
to decide which factors to be retained: (1) minimum
eigenvalue of 1; (2) deleting items with factor loadings less
than 0.5 on all factors, or greater than 0.5 on two or more
factors; (3) a simple factor structure; (4) scree test. Items
that did not success these rules were excluded. Table 1
shows all factors with their number of items, eigenvalue,
explained variance.
scree test in Figure-1 show ‘ deflect ’ at 6 calling for
retaining 5 factors.
Factor Label
Number
of items
Eigen
-
value
Explained
variance
(%)
1 Computer self-efficacy
12 6.644 17.268
2
Attitudes toward technology and
e-learning 5 4.370 14.427
3
Computer-internet experience 6 2.984 10.028
4 Tech nology-internet quality 5
2.265 6.958
5
Intention to use technology and e-
learning 3
1.764 5.946
54.63
Figure-1: scree plot
Table 2 shows the factor loading of the items with a loading
of 0.40 or greater.
4. The result
The result show that, 9 items (from construct1 - CSE) in
factor1 had a loading ranging from .503 to .797 and 3 items
were eliminated, 6 items (from construct2 - ATE) inFactor 2
had a loading from 0.716 to 0.937 and one item excluded, 6
items (from construct3 - CIE) in factor 3 had loading from
.597 to .790, 3 items (from construct4 - TIQ) in factor 4 had
loadings from .806 to .823, and 2 items were eliminated,and 3
items (from construct5 - IUT) in factor 5 had loading from
.532 to .622, all the items have been accepted are positively
worded.
Consequently, we could accept the 27 items with explained
variance (54.63%) and identify the 5 factors – Factor1
involving 9 items that were related to the attributes of
computer self-efficacy, factor2 contains 6 items related to
Table-1: Identified factors with number of items,
eigenvalue, and explained variance

attitudes toward technology and e-learning, factor3 linking 6
items that related to computer-internet experience, factor4
involving 3 items that were related to technology-internet
quality, and factor5 linking 3 items that related to intention
to use technology and e-learning.
5. Optimization of the Scale
The factor analysis identified 27 items in five groups, as
Factor1, Factor2, Factor3, Factor4, and Factor5, the
Cronbach’s reliability was tested for the 27-item scale and
found .801, after that, we investigated extra optimization of
the instrument by examining the reliability coefficient of
each factor independently.
We found that, the 9-item Factor1 had a reliability coefficient
of .897, 6-item Factor2 had reliability coefficient of .941, 6-
item Factor3 had reliability coefficient of .788, 3-item
Factor4 had reliability coefficient of .729, and 3-item Factor5
had reliability coefficient of .739. Thus, indicating high inter-
item correlation within all the factors and indicating that
these factors could be used to involve an instrument to
measure students’ intention to use technology and e-
learning.
6. Conclusion and future research
The results of this study demonstrate that this developed
instrument is an initial tool to assess intention to use, other
extra variables included in future studies may support or
affect our result, as well using different sample ( size,
quality) could influence orstrengthens ourresult. The result
of suchstudywould informpolicymakersandauthoritiesfor
planning and curriculum development purposes in Libyan
higher education. Finally, with technology use in higher
education becoming wide spread globally, a comparison
studies could be conduct between countries or cultures to
identify the culture variables that influence faculties’
intention to use technology and e-learning.
Component
1 2 3 4 5
Computer self-efficacy (CSE)
CSE1: How confident do you feel
when you scrolling around the
monitor screen.
.797
when you using internet search
engines.
.781
when you finishing the Internet
program
.767
when you printing materials from
the Internet,
.749
when going to next pages using
‘‘forward’’ button
.737
when you going To previous
pages using ‘‘back’’ button
.721
using the internet
.681
CSE8: How confident do you
feel when you click on the
screens you want
.664
feel when you click on the
screens you want
.617
feel when downloading or
upload materials from internet
feel when you selecting right
terms for Internet search
feel when locating necessary
information on the internet
-
.503
.521
-.571-
-.545-
Attitudes toward technolo-
gy and e-learning (ATE)
ATE1: I believe using internet
is helpful for learning
.937
ATE2: I believe that it gives me
a feeling of psychological stress
greatly
.917
ATE3: I believe that it is only
advisable for people with a lot
of patience
ATE4: I know that it is very
difficult
.898
.881
Table-2: factor loading
Rotated Component Matrixa

Component
1 2 3 4 5
ATE5: I know that it is very
complicated
.862
ATE6: I believe that it makes a
person more productive at
his/her job
.716
ATE7: I believe that Traditional
face-to-face learning is more
familiar than e-learning
-
Computer-internetexperie-
nce (CIE)
CIE1: How often do you use
internet browser
.790
CIE2: How often do you use
internet for information search
.742
CIE3: How often do you use e-
mail
.688
CIE4: How often do you
download free software
.671
CIE5: How often do you use the
word processing program.
CIE6: How often do you listen
to audio and watch video
.618
.597
Technology-internet quali-
ty (TIQ)
TIQ1: How satisfied are you
with the communication quality
of the Internet
.823
with “There are some
difficulties on connecting the
internet at any place/time
.808
with the internet fee
.806
with the speed of the Internet
TIQ5: How satisfied are you in
general with the information
technology infrastructure
-
-
Intention to use technology
– e-learning(IUT)
IUT1: I am willing to
participate in learning courses
opportunities using internet and
technology
.622
IUT2: I think learning using
internet and technology should
be implemented in classes
.606
Component
1 2 3 4 5
IUT3: I intend to use
technology and Internet to
assist my learning
.442 .532
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 7 iterations.
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