JTUS, Vol. 02, No. 1 January 2024 8
JTUS, Vol. 02, No. 1 January 2024
E-ISSN: 2984-7435, P-ISSN: 2984-7427
Digital Inequality: E-learning Outcomes among Youth in
Indonesia
Umar Halim
1
, Diah Febrina
2
, Anna Agustina
3
, Nurul Hidayat
4
, Widia Ningsih
5
1,2,3,4
Universitas Pancasila, Indonesia
5
Universitas Islam Negeri Sunan Gunung Djati, Indonesia
Email: umarhalim@univpancasila.ac.id, diahfebrina@univpancasila.ac.id,
annaagustina@univpancasila.ac.id, nurulhidayat@univpancasila.ac.id,
widianingsih63[email protected]
Abstract
The Covid-19 pandemic reinforces digital inequality in education ecosystem. The purpose of
this study is to determine the digital inequality of E-learning Outcomes among Youth in
Indonesia. This study takes a digital inequality theory and analyzes its impact on e-learning
outcomes. A quantitative research approach with a set of questionnaires was used. Data for this
study was collected from 407 respondents, whose ages ranged from 15 to 23 years, all residing
in Jakarta and surrounding areas (Jabodetabek), the capital of Indonesia. The results show that
digital literacy (ᐃR²= 3%, ß=0.202) and academic-related usage (ᐃR²=27.2%, ß=0.425) are a
positive contribution to the e-learning outcomes. The results suggest that lecturers and policy
makers must increase e-learning outcomes through academic-related usage and digital literacy.
keywords: Digital Inequality, Digital Literacy, Academic-Related Usage, E-Learning Outcomes,
Access to ICT
INTRODUCTION
Digital inequality still exists in the education sectors (Inan Karagul et al., 2021; Jena, 2020).
Not only in developing countries, but it also occurs in developed countries. In the United States
(Katz et al., 2021), found internet connectivity and digital device as a challenge for students during
remote learning. Meanwhile, Pakistan is experiencing problems with the lack of internet
technology, connections and infrastructure (Park et al., 2021). Likewise in India, Jena (2020) found
that the gap was still due to limited internet access and ownership of laptop/computer/gadget
devices in their homes. In Indonesia, problems related to unequal access and poor bandwidth
have an impact on inequality in the digital education landscape (Unicef, 2021).
However, the material or physical factor is not enough to solve the problem of inequality
in education. The digital inequality theory emphasizes that even though everyone already has
internet access, inequality between them will still exist due to the digital skills or literacy and usage
Digital Inequality: E-learning Outcomes among Youth in Indonesia
JTUS, Vol. 02, No. 1 January 2024 9
(Hargittai et al., 2019). Van Deursen & Helsper, (2018) confirmed that what users do online, and
their skills will affect the outcome of internet use. In line with Nash, (2020) which found that the
main concern in the academic field is developing digital literacy so that users (lecturers and
students) can use online platforms.
Martínez-Cantos, (2017) said that competences and digital literacy become an important
thing for the development of information society. Jang et al. (2021) argue that digital literacy
influences individuals’ intention to use digital technology for learning among young people in
Korea and Finland. Among Thai students, Techataweewan & Prasertsin (2018) found that digital
literacy has a positive effect on learning performance.
Students need to have digital literacy to balance technological developments, because
technology is dynamic and constantly changing (Zilian & Zilian, 2020) (Purnama et al., 2021). In
general, the skills to manage multifunctional technology will also help individuals in continuously
upgrading their knowledge and competencies (Martínez-Cantos, 2017), as well as engaging in
social life (Büchi & Vogler, 2017).
In addition to digital skills, various uses of the internet are also important contributors to
reap benefits (Van Deursen, 2020) (Van Dijk, 2017). Students in Bangladesh feel that internet use
positively improved academic performance and improved their quality of life (Fatema et al., 2020).
Unfortunately, there are still few studies that examine simultaneously the influence of digital
literacy factors and usage in an educational context on improving e-learning outcomes. These two
factors are an illustration of the second level of digital divide (Van Deursen, 2020), or also known
as digital inequality (Katz et al., 2021).
This study aims to examine the impact of digital inequality theory on e-learning outcomes.
Both variables from digital inequality, digital literacy and usage were tested simultaneously on e-
learning outcomes, so that it was known whether they had an impact and which variable
contributed more to improving e-learning outcomes. This study also analyzes the extent to which
access to ICT has a positive impact on e-learning outcomes.
METHODS
This study used a quantitative method through an online survey to 407 adolescents aged
15-23 years, all residing in Jakarta and surrounding areas (Jabodetabek), the capital of Indonesia.
A set of questionnaires has been distributed during November 2021 to respondents through
purposive sampling technique. Questions in the questionnaire to determine demographics, access
to digital technology, digital literacy, academic-related usage and e-learning outcomes with
closed questions. Data were analyzed using regression test to see the contribution of digital
literacy and academic-related usage variables to e-learning outcomes.
Measures
Digital Literacy
Umar Halim, Diah Febrina, Anna Agustina, Nurul Hidayat, Widia Ningsih
10 JTUS, Vol. 02, No. 1 January 2024
The digital literacy is defined as the individual's ability to find and produce all forms of
information that can solve problems for themselves and others. This study measured the digital
literacy variable with 10 items (Table 1). Exploratory factor analysis was conducted to explore the
latent factor structure. For Digital Literacy, the Kaiser-Meyer-Olkin (KMO) measure was .909 and
Bartlett's test of sphericity was significant (X
2
=3270.896, p<0.001), indicating adequacy of the
sample. As such, two factors emerged with eigenvalues greater than 1.00. The two-component
solution explained a total of 74.8% of the variance, with Component 1 contributing 63.5% and
Component 2 contributing 11.3%.
Table 1 Rotated Component Matrix of Digital Literacy
Item
Consuming
Prosuming
To access
.872
.278
To select
.834
.305
To understand
.832
.224
To analyze
.818
.333
To verify
.741
.431
To evaluate
.726
.467
To distribute
.260
.863
To produce
.277
.830
To participate
.299
.734
To collaborate
.381
.734
Cronbach’ Alpha
.933
.866
Based on the test results in table 1, the first factor is identified as the consuming dimension
and the second as prosuming. The consuming dimension (CA= .937, M= 3.53) was measured to
identify the respondent's ability to use the internet to obtain the data/information needed, while
the prosuming dimension (CA=.866, M=3.20) measured the respondent's ability to use the
internet to produce content and participation. The respondents were given the answers with 5
scales (“Very Low = 1” to “Very High = 5”). The consuming literacy is measured by statement items
such as “the ability to choose the information needed” and prosuming literacy is measured by the
item “the ability to create information content that is shared through WhatsApp Group, blogs or
websites.” Overall, the digital literacy has a Cronbach' Alpha value of .933 with a mean value of
3.39, and a standard deviation of .715.
Academic-Related Usage
Digital Inequality: E-learning Outcomes among Youth in Indonesia
JTUS, Vol. 02, No. 1 January 2024 11
The academic-related usage is defined as the use of the internet to meet the needs of the
learning process. A total of 12 items have been tested for factor analysis (Table 2). For academic-
related usage, the Kaiser-Meyer-Olkin (KMO) measure was .915 and Bartlett’s test of sphericity
was significant (X
2
= 3498.949, p<0.001), indicating adequacy of the sample. As such, three factors
emerged with eigenvalues greater than 1.00. The three-component solution explained a total of
71.6% of the variance, with Component 1 contributing 53.4%, Component 2 contributing 9.8%
and Component 3 contributing 8.4%.
Table 2 Rotated Component Matrix of Academic-Related Usage
Item
Communication
Information
Digging
Discussing with friend about assignment
.872
.192
Discussing with friend about lecturer
material
.846
.275
Sharing references about course material
with friend
.732
.334
Asking a friend for course material
.741
.228
Seeking information to complete
assignment
.667
-.037
Seeking materials to do assignment
.644
.028
Downloading e-book for course material
.266
.144
Seeking references in YouTube for
course material
.200
.326
Downloading articles for assignment
.497
.061
Seeking video tutorial for use Ms.Office
.037
.509
Asking a lecturer for course material
.146
.856
Asking the campus staff for class
schedules
.412
.704
Cronbach’ Alpha
.912
.745
Based on the Rotated Component Matrix table, academic-related usage is divided into three
factors: communication (Cronbach Alpha= .912, M=3.18), sources of information (Cronbach
Alpha= .827, M=3.47) and information digging (Cronbach Alpha). = .745, M=2.74). The
Communication reviewed the extent to which the internet was used as a communication tool in
the learning process. An example of the item being measured was "asking school materials to a
Umar Halim, Diah Febrina, Anna Agustina, Nurul Hidayat, Widia Ningsih
12 JTUS, Vol. 02, No. 1 January 2024
friend". The dimension of the information source means that the internet was used as a source of
information/knowledge related to the learning process. An example of the item being measured
was “looking for reading sources to do school/college assignments.” Information Digging related
to communication with internal school/university parties. Overall, this variable got a Cronbach
Alpha value of .916 and a Mean value = 3.52 (not in the table).
E-Learning Outcomes
The e-learning outcomes variable in this study is defined as the academic benefits obtained
from using the internet. This variable is measured by eight items and a factor analysis test was
performed (Table 3). For e-learning outcomes, the Kaiser-Meyer-Olkin (KMO) measure is .932 and
Bartlett’s test of sphericity was significant (X
2
=2582.599, p<0.001), indicating adequacy of the
sample. As such, one factor emerged with eigenvalues greater than 1.00, explaining 69.9% of the
variance respectively. The Cronbach' Alpha value for this factor is .938 and the Mean value is 4.27.
Examples of question items such as "the internet makes it easier for me to discuss with a group
of friends" and "the internet makes it easier for me to get sources/references that are relevant to
school assignments." Respondents were given five answers (5 points scale: “Strongly Disagree” to
“Strongly Agree”).
Table 3 Component Matrix of E-Learning Outcomes
Item
Component
The internet makes it easier for me to
discuss with friends
.786
Internet help me to find lecturer
material
.819
Internet help me to explain course
material for my friend
.819
Internet help me to join course at any
where
.842
The internet makes it easy to complete
task
.890
The internet makes it easy to get
resources
.876
Platform available on the internet help
me to complete my college
assignments on time
.844
The reading resources Which I Get
from the internet help me better
understand school material
.809
Digital Inequality: E-learning Outcomes among Youth in Indonesia
JTUS, Vol. 02, No. 1 January 2024 13
Item
Component
Cronbach’ Alpha
.938
RESULTS AND DISCUSSION
The purpose of this study is to examine the factors that influence e-learning outcomes. In
addition, to identify the effect of Access to ICT on e-learning outcomes. Table 4 describes the
categories of respondents. The data obtained showed that most of the respondents were women
(58%) compared to men. The age category is dominated by respondents aged 18 -24 years (61.3%)
and the education category is more of respondents who are studying at the University level (60%)
than students at Senior High School. Regarding the economy of parents, the average student's
parents earn 3 - 5 million rupiah / US 200 to US 350 (34.2%) and 9.3% of respondents' parents
earn less than 1 million rupiah / US 66. Relating to the time to access the internet most of the
respondents spent an average of 4 to 7.59 hours per day (34.9%).
Table 4 Respondent Categories
N
%
Gender
Male
171
42
Female
236
58
Age
15 – 17
154
38.7
18 – 24
253
61.3
Education
Senior High School
163
40
University
244
60
Parent’s Income
Less than Rp. 1 million
38
9.3
1 - 2.9 million
86
21.1
3 - 5 million
139
34.2
5.1 - 8 million
59
14.5
8 million more
85
20.9
Umar Halim, Diah Febrina, Anna Agustina, Nurul Hidayat, Widia Ningsih
14 JTUS, Vol. 02, No. 1 January 2024
Time for Internet Access
< 4 hours
22
5.4
4 - 7.59 hours
142
34.9
8 - 11.59 hours
122
30
> 12 hours
121
29.7
Table 5 shows that to access digital technology, most respondents use smartphones (M =
3.63) compared to personal computers (M = 1.49). Likewise, for the purposes of the learning
process, respondents use smartphones more (M = 3.55) than other devices. Another result shown
is that respondents spend more time using chat applications (M= 3.96), social media (M= 3.94)
and watching movies (M=3.66).
Table 5 Access to ICT
Mean
SD
Access to ICT
Smartphone
3.63
.513
Laptop
2.89
.950
Tablet
1.37
.760
Personal Computer
1.49
.888
Access to ICT for E-Learning
Smartphone
3.55
.625
Laptop
3.06
1.020
Tablet
1.27
.688
Personal Computer
1.38
.781
Access to Platform
Search Engine
2.8
1.341
Email
3.21
.916
Social Media
3.94
.729
Chatting Applications
3.96
.816
Online Media
2.68
1.023
Digital Inequality: E-learning Outcomes among Youth in Indonesia
JTUS, Vol. 02, No. 1 January 2024 15
e-Shopping
2.97
1.063
Game Online
2.57
1.271
Watching Movie
3.66
.918
For the hypothesis, multiple regression analysis was used to analyze the contribution of
digital literacy and academic-related usage to e-learning outcome (table 6). The test results show
that digital literacy and academic-related usage have a positive and significant impact on e-
learning outcomes, H1 is supported. Academic-related usage variables accounted for 27.2% and
digital literacy by 3%. The overall contribution is 30.2%. The results of the regression test also
show that an increase in every 1 unit of digital literacy will contribute 0.202 units of improvement
in e-learning outcomes (β=0.202 p<0.05), while 1 unit increase in academic-related usage
(β=0.425 p<0.05) contributes 0.425 e-learning outcome units.
Table 6 Multiple Regression of Academic-related usage and digital literacy to E-Learning
Outcomes
Factor
R
2
adjusted
Beta
sig
Academic-Related Usage
0.272
0.425
0.001
Digital Literacy
0.030
0.202
0.001
In other testing, regression analysis was used to identify the contribution ICT Infrastructure
to e-learning outcomes (Table 7). The results of the regression test show that Access to ICT
positively contributes to E-Learning outcomes, H2 is supported. An increase in 1 unit of Access to
ICT will contribute as many as 0.272 units of e-learning outcomes. Other test results from each
device of ICT found that smartphone and laptop use positively and significantly contributed to E-
Learning outcomes. The results of the regression test showed that the largest contribution was
the use of laptops (β=0.252 p<0.05) compared to smartphones (β=0.140 p<0.05).
Table 7 Regression Analysis between Access to ICT and E-Learning Outcomes
Beta (β)
Sig
Access to ICT
0.272
.001
Smartphone
0.140
.004
Laptop
0.252
.001
Tablet
0.041
.452
Personal Computer
0.001
.987
The digital inequality perspective has been used to review e-learning outcomes. This study
explains that the disparity of digital literacy and academic use determines the benefits of online
learning. This finding reinforces the study of scholars that the benefits of the internet depend on
Umar Halim, Diah Febrina, Anna Agustina, Nurul Hidayat, Widia Ningsih
16 JTUS, Vol. 02, No. 1 January 2024
the level of digital literacy and usage (Hargittai et al., 2019). This study found that the contribution
of academic-related usage was greater in encouraging the improvement of e-learning outcomes.
The academic-related usage likes to increase information seeking for assignment and course
material needs to be encouraged to teenagers. In addition, getting used to communication in the
form of online discussions about academics in the teaching and learning process is important.
Teachers and academic staff also need to open communication spaces for students so that they
get solutions to the academic problems they face.
However, digital literacy is important even though the contribution to e-learning outcomes
is not large, especially related to how to validate and evaluate the information obtained. Both
items will help teenagers get correct and useful information for academic purposes. The
dimension of prossuming among adolescents needs to be increased through participation and
collaboration activities. Educational institutions need to encourage lecturers to design academic
assignments that can involve students in groups and increase student activity.
Another result of the four types of media that were asked, adolescents more often use
smartphones in their learning activities. However, it was found that it was laptops that had more
impact on the e-learning outcome variant. This needs attention to policy makers to improve
infrastructure by providing adequate laptops in educational institutions.
CONCLUSION
Based on the results of research and data processing, it was concluded that of the four types
of media that were asked, adolescents more often use smartphones in their learning activities.
However, it was found that it was laptops that had more impact on the e-learning outcome variant.
This needs attention to policy makers to improve infrastructure by providing adequate laptops in
educational institutions.
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Umar Halim, Diah Febrina, Anna Agustina, Nurul Hidayat, Widia Ningsih (2023)
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