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S. Mahabub Basha, M. Kethan. (2022). Covid-19 Pandemic and the
Digital Revolution in Academia and Higher Education: an Empirical
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Eduvest –Journal of Universal Studies
Volume 2 Number 8, August, 2022
p-ISSN 2775-3735-e-ISSN 2775-3727
COVID-19 PANDEMIC AND THE DIGITAL REVOLUTION IN
ACADEMIA AND HIGHER EDUCATION: AN EMPIRICAL
STUDY
S. Mahabub Basha
1
, M. Kethan
2
International Institute of Business Studies (IIBS) Bangalore, India
Email: [email protected], dr.mkethan@iibsonline.com
ABSTRACT
While learning online during the pandemic, students faced so many problems, difficulties,
and challenges with respect to stress, worry, and anxiety, technology adaptability, course
content delivery, and the digital transformation from a physical classroom to an online
mode amidst the pandemic. This research aims to explore challenges faced by students
using a student online survey, and data was analyzed using SPSS for students' online
learning experiences amidst the pandemic, which was handled adequately. The extraction
of common factor variances from measure sets for prominent factors to measure the
transformational shift from offline to online digital learning was done using exploratory
factor analysis. Sampling adequacy measurement test for KMO A correlation matrix that
indicates whether the variables are unrelated in Bartlett’s sphericity test, in which the level
of significance gives the test result, showed in the present study that significant
relationships exist among variables and there is high correlation. Principle Component
Analysis (PCA) with Varimax Rotation used for challenges showed inter-correlation and a
significant relationship between the challenges students faced in digital mode with a
significant P value at the 5% level of significance. Regression analysis of the H0 variable's
relationship with one or more variables revealed a significant constant value for
psychological, technological, and personal opinion as three representative factors found to
be significant, indicating that the H0 is rejected.
KEYWORDS
Challenges, Education, Learning Online, Pandemic, Tools
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S. Mahabub Basha, M. Kethan
Covid-19 Pandemic and the Digital Revolution in Academia and Higher Education: an
Empirical
Study 1.649
INTRODUCTION
As stated by Sáiz-Manzanares et al (2019) and Leszczyński et al (2018) digital
transformation in higher education is not new, and as a relevant subject, education
stakeholders must be concerned about and train professionals to tackle obstacles. This
transition aids in the adjustment to new technology (Abad-Segura et al., 2020) and Covid-
19 modifications. The sum of all digital procedures required to achieve a transformation
that enables educational institutions to use digital technology in an optimal and beneficial
manner is referred to as "digital transformation." It is a process that necessitates strategic
planning, building trust, merging ideas, and strengthening the parties involved, as well as
collaboration and knowledge separation inside the business (Cameron & Green, 2019).
According to Hitz & Turnoff (2005), digital transformation has shifted physical teaching
to online hybrid learning, and digital technology has labeled this process of replacement as
disruptive. The COVID-19 pandemic triggered the digital transformation, resulting in
several pieces of legislation being quietly introduced within a few days (Strielkowski,
2020), giving the online learning brand a messiah status from a disruptive process. Bozkurt
& Sharma (2020) say that online education comprises online teaching and learning.
Careful preparation for design and instruction, as well as the use of a well-organized
model at the design and instruction level, is a must for online learning to be effective, and
instead of online education, emergency remote teaching was used (Vlachopoulos, 2011;
Bozkurt & Sharma, 2020). Many people in society and the educational community have
expressed concerns regarding the quality of online learning. In the context of satisfaction,
a student's perception of their experience acts as a substitute for learning engagement
(Swan, 2002; Arbaugh, 2001; Richardson, 2001). Most students see information as a
commodity that can be freely traded within the learning community and is critical to
academic outcomes. Modern technologies are responsible for traditional classroom
boundary resolution. Norberg (Dziuban & Moskal, 2011) developed a time-based blended
learning model that altered the role of the instructor, whereas Liu & Hwang (2010) focused
on student preferences in the learning environment. As indicated by the students, they live
in a highly engaged world and have similar expectations of their lectures. Students evaluate
online learning on the importance of teacher presence, and (Kuo et al., 2013) state that both
face-to-face and online learning play an important role. While learning online, Francisco
et al (2012) found that demographics and culture had an impact on interaction strategy
design.
The COVID-19 pandemic caused logistical challenges in the instructors' and
learners' attitudes, as noted by Kara & DeShields (2004) and research identifying the
factors influencing the students' satisfaction was needed. According to (Appleton-Knapp
& Krentler, 2006), evaluating students' needs and expectations would improve their
satisfaction. Smart and Cappel (2006) indicated that variable identification affected
students' satisfaction with online learning. Kopp et al., (2019) while evaluating the
assumption of digital transformation, identified obstacles related to changes, pace,
technology competency, and finance. The educational use of technological tools and
devices on the internet is called "online learning," as stated by Neans et al., (2009) and the
increasing innovation in technology access to the internet has motivated "learning online,"
as added by (Tang & Byrne, 2007). It is debatable whether online learning can replace face-
to-face instruction (Aminger et al., 2021). Many critical challenges affecting online
learning stemmed from the instructor's evaluation of students' academic integrity
(Algahtani et al., 2020). Cyber bullying or stalking (Bond et al., 2018) no internet access,
low quality instructional Delivery Stein (2020), professional training in technology access,
Eduvest – Journal of Universal Studies
Volume 2 Number 8 , August 2022
1.650 http://eduvest.greenvest.co.id
(Sandkuhl & Lehmann, 2017) in accessible tools and technology issues challenges related
to customizing lectures and online assessment tools. Cochrant (2016) found that online
instruction skills are the foundation of online environment interaction, as added by Sáiz-
Manzanares et al (2019). that many learners prefer custom or personalized video lectures
that help them learn. COVID-19 had a negative impact on students' learning activities.
Carnaghan & Webb (2007) investigate the impact of students' COVID-19 performance on
their learning achievement and learning approach. Arbaugh (2007) included factoring
methods and retrieved primary constructs exhibiting excellent reliability. Stewart Hong
(2004) used the principal component analysis, and the dimension of complexity was found
to define student satisfaction in online learning. Elements related to online evaluation like
active interaction, task time, and the cooperation of students were found by Akdemir &
Koszalka (2008) who, by using exploratory and confirmatory factor methods, validated
their previous findings. Classification and repression trees were used by (Dziuban &
Moskal, 2011), like facilitation, information and concept communication, and student
concern and respect. Guttman (1954) investigated student perceptions of online learning
using image analysis, where one general component was constant across all modalities.
Dziuban et al (2013) investigated challenges linked to campus resources for learners'
support and identified different designs, instructions, and delivery methods to encourage
students' learning desires. In Armstrong's (2011) research on online learning, students
preserved the positive attributes of technology. Students were more comfortable when
learning was done face-to-face (Zhang Peris, 2004). Factors contributing to online student
satisfaction include clear and relevant assignment and communication, campus-based
resource access, technical support availability, and course equipment and technology
orientation. In an online setting, student and faculty support, as well as an appreciation for
preparation, are required. Factors related to perceived assessment fairness and personal
cognitions' impact were reported by Branch & Dousay (2015). Muhammad et al (2020)
provided learners with computer anxiety, ease of use, course quality, and e-learning
assessment diversity.
While learning online during the pandemic, students faced so many problems,
difficulties, and challenges with respect to stress, worry, and anxiety, technology
adaptability, course content delivery, and the digital transformation from online mode to
face-to-face classroom learning. The purpose of this research is to discover the problems,
difficulties, and challenges that students encountered while learning online.
Respondents in the study are limited to only Goa residents who participated in this
study. It is not fully representative of the general population throughout India.
RESEARCH METHOD
The sample frame will be limited to Goan students in the age categories of 13–15
(28%), 16–18 (27%), 19–21 (27%), and 22 and above (18%). 8th–10th grade STD (29%),
XII (27%), UG (27%), and PG (18%) were the classes I took. The sample size includes a
total of 300 usable responses from students of different age categories and levels of
education. The research design used is a descriptive research design. The selection of
respondents will be done by purposive sampling (non-probability). Source of information:
An online survey is used to collect primary data in an electronic format. The form sought
data on problems, difficulties, and challenges the students face in the online mode of
education following the preliminary data search from secondary data that was collected
through internet-based resources.
Covid-19 Pandemic and the Digital Revolution in Academia and Higher Education: an
Empirical Study 1.651
The source of secondary data is taken from research papers, articles, journals, online
sites, and other sources available on an online or offline platform. The form was
WhatsApped or emailed to a cross-section of the general student population selected
randomly. The survey was restricted to respondents in Goa only. The valid responses
received to the online form totaled 300. The survey data was then coded and tabulated for
ease of analysis using SPSS software. Descriptive statistical analysis was carried out, and
on the basis of the solutions obtained, observations and insights were developed. Finally,
to represent the analysis in an understandable manner, tables were prepared.
RESULT AND DISCUSSION
A. Data Analysis
Internal consistency of the research instrument used to collect data reliability tests
has been undertaken, and the Cronbach’s alpha is 0.918 for 17 items, which means the data
is reliable to the extent of 91.8%. The extraction of common factor variances from measure
sets is called exploratory factor analysis, which the present study uses to obtain factors
prominent in measuring the transformational shift from offline to online digital learning.
The sampling adequacy test of the K-M-O (Kaiser-Meyer-Olkin) value while performing
factor analysis to confirm whether the sample size chosen for the study is adequate is 0.907.
Any value above 0.70 is a good value and confirms the adequate sample size according to
Kaiser and Rice (1974), and the 0.907 obtained is sufficient for a factor analysis. The table
shows three factors derived from the 17 variables used, and three representative factors are
given suitable names as per the group components. With a KMO of 0.907, the
appropriateness of the factor analysis is confirmed. A correlation matrix that indicates
whether the variables are unrelated in the Bartlett’s sphericity test, in which the significance
level gives the result of the test, shows that in the present study the significance level has a
very small value of 0.00, which is less than 0.05, suggesting that there is a significant
variable relationship and also that variables are highly correlated.
Table 1 KMO Bartlett Test
0.907
Bartlett's Sphericity Test
Chi-Square Approx.
2739.709
df
136
Sig.
0.000
Source: Primary Data
Eduvest – Journal of Universal Studies
Volume 2 Number 8 , August 2022
1.652 http://eduvest.greenvest.co.id
Table 2 Rotation Sums of Squared Loadings
Component
Rotation Sums of Squared Loadings
Total
% of Variance
Cumulative %
1 Psychological
4.223
24.843
24.843
2 Technology
3.171
18.654
43.497
3 Personal Opinion
3.067
18.041
61.538
Source: Primary Data
Table 3 Rotated Component Matrix
a
Rotated Component Matrix
a
Component
1
2
3
1) I experience fear in virtual learning
0.778
2) I get worried during online learning
0.743
3) I experience anxiety during online learning
0.742
4) I experience hopelessness during online learning
0.699
5) The learning effectiveness is less online compared to
face-to-face
0.671
6) I experience anger during online learning
0.654
7) Comprehension of material becomes a challenge in
online
0.558
8) I struggle in handling the electronic gadgets during
online learning
0.743
9) I face problem with the computer
0.731
10) Planning of study schedule becomes difficult in online
learning
0.678
11) The relative learning becomes difficult in online learning
0.677
12) I face difficulties with the video during online learning
13) I see a problem in teacher and students interaction
0.747
14) I face problems, difficulties and challenges in online
mode
0.694
15) I am being challenged with the delivery of material
0.670
S. Mahabub Basha, M. Kethan
Covid-19 Pandemic and the Digital Revolution in Academia and Higher Education: an
Empirical
Study 1.653
16) There is no clarity of explanation in online learning
content
0.647
17) I feel isolated during online classes
0.642
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with
Kaiser Normalization.
a. Rotation converged in 8 iterations.
Source: Primary Data
For challenges in this study, Principle Component Analysis (PCA) with Varimax
Rotation was used. Bartlett’s sphericity test (chi-square value: 2739.709, p 0.05) showed
inter-correlation between variables for PCA. PCA application for issues and challenges
confronted by students while in digital online learning Results showed three factors having
an EV > 1, which indicates a three-component solution. A total of seventeen statements
were made, and the statement "I face difficulties with the video during online learning"
didn’t get a loading, so it was omitted. The psychological factor as the first factor explained
a 24.843% variance with seven variables; the second factor, the technology factor,
comprised four variables and delineated 18.654% of the variance; the third factor, the
personal opinion of online learning, had five variables, i.e., and the third factor described
18.041% of the total variance that is illustrated in Table 2. From this table, three factors
have been obtained: psychological, technological, and personal opinion, with the total
variance explained by the variables at 61.538%.
A Statistical tool is used to test H0 relationship between a variable with one or more
than one variables using Regression analysis.
Table 4 Summary Model
Summary Model
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
0.763
a
0.583
0.579
0.624
a. Predictors: (Constant), REGR factor score 3 for analysis 1, REGR factor score 2 for
analysis 1, REGR factor score 1 for analysis 1
Table 5 ANOVA
a
ANOVA
a
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
160.304
3
53.435
137.438
0.000
b
Residual
114.693
295
0.389
Total
274.997
298
a. Dependent Variable: Challenges faced while learning in online mode of education
b. Predictors: (Constant), REGR factor score 3 for analysis 1, REGR factor score 2 for
analysis 1, REGR factor score 1 for analysis 1
Eduvest – Journal of Universal Studies
Volume 2 Number 8 , August 2022
1.654 http://eduvest.greenvest.co.id
Table 6 H0 Result
H0: Challenges faced does not have a significant relationship with student’s online
learning during pandemic
Dependent Variable: Challenges faced while learning in online mode of education
Adjusted R square : 0.579 F value: 137.438 P value: 0.000
Sr No
Independent Variable
Beta value
T value
Sig
(Constant)
111.020
0.000
1
Psychological Factor
-.152
-4.039
0.000
2
Technology Factor
0.281
7.460
0.000
3
Personal Opinion Factor
0.694
18.448
0.000
Source: Primary Data
The above Table 6 shows that there exists a significant relationship between the
challenges faced by students in digital learning mode and the P value, which is quite
significant at the 5% significance level. The R square obtained was 0.583, indicating that
the existing model is explained to the extent of 58.3% with an F value of 137.438. With a
significant constant value, psychological, technological, and personal opinion factors are
found to be significant at the 5% level of significance, so the H0 stands rejected.
CONCLUSION
In this digital transformation, where there is a shift from physical to online mode,
instructional technology has played an important role as a cushion effect in terms of online
learning. With hardly any prior planning and design instruction, the education system
witnessed a rude shock due to the sudden pandemic, and the methods adopted in teaching
and learning were a crisis response. Online learning assessed digital competency by
combining elements of technology-driven learning and internet reliance with a lack of
consistency in learning models and technological tool application. The shift to digital
transformation provided challenges that, if factors were properly identified, could
transform into opportunities at the psychological, technological, and personal growth
levels. With the right internet connection, literacy, tool compatibility, and high-tech
change, the problems that were found could be solved.
REFERENCES
Abad-Segura, E., González-Zamar, M.-D., Infante-Moro, J. C., & Ruipérez García, G.
(2020). Sustainable management of digital transformation in higher education: Global
research trends. Sustainability, 12(5), 2107.
Akdemir, O., & Koszalka, T. A. (2008). Investigating the relationships among instructional
strategies and learning styles in online environments. Computers & Education, 50(4),
1451–1461.
S. Mahabub Basha, M. Kethan
Covid-19 Pandemic and the Digital Revolution in Academia and Higher Education: an
Empirical
Study 1.655
Algahtani, H., Shirah, B., Subahi, A., Aldarmahi, A., Ahmed, S. N., & Khan, M. A. (2020).
Perception of students about E-learning: a single-center experience from Saudi
Arabia. Dr. Sulaiman Al Habib Medical Journal, 2(2), 65–71.
Aminger, W., Hough, S., Roberts, S. A., Meier, V., Spina, A. D., Pajela, H., McLean, M.,
& Bianchini, J. A. (2021). Preservice secondary science teachers’ implementation of
an NGSS practice: Using mathematics and computational thinking. Journal of
Science Teacher Education, 32(2), 188–209.
Appleton-Knapp, S. L., & Krentler, K. A. (2006). Measuring student expectations and their
effects on satisfaction: The importance of managing student expectations. Journal of
Marketing Education, 28(3), 254–264.
Arbaugh, J. ben. (2001). How instructor immediacy behaviors affect student satisfaction
and learning in web-based courses. Business Communication Quarterly, 64(4), 42–
54.
Bond, M., Marín, V. I., Dolch, C., Bedenlier, S., & Zawacki-Richter, O. (2018). Digital
transformation in German higher education: student and teacher perceptions and
usage of digital media. International Journal of Educational Technology in Higher
Education, 15(1), 1–20.
Bozkurt, A., & Sharma, R. C. (2020). Emergency remote teaching in a time of global crisis
due to CoronaVirus pandemic. Asian Journal of Distance Education, 15(1), i–vi.
Branch, R. M., & Dousay, T. A. (2015). Welcome to Jacksonville and the 2014 AECT
International Convention. Sat, 10, 9–15.
Cameron, E., & Green, M. (2019). Making sense of change management: A complete guide
to the models, tools and techniques of organizational change. Kogan Page Publishers.
Carnaghan, C., & Webb, A. (2007). Investigating the effects of group response systems on
student satisfaction, learning, and engagement in accounting education. Issues in
Accounting Education, 22(3), 391–409.
Dziuban, C., & Moskal, P. (2011). A course is a course is a course: Factor invariance in
student evaluation of online, blended and face-to-face learning environments. The
Internet and Higher Education, 14(4), 236–241.
Dziuban, C., Moskal, P., Kramer, L., & Thompson, J. (2013). Student satisfaction with
online learning in the presence of ambivalence: Looking for the will-o’-the-wisp. The
Internet and Higher Education, 17, 1–8.
Francisco, G. G., Jorge, G., Oscar Martin, R., & Miquel Angel Motero, A. (2012). Gender
differences in E-learning Satisfaction. Computers & Education, 58(1), 283–290.
Hitz, S., & Turnoff, M. (2005). Education goes digital; the evolution of online learning and
the revolution in higher education. Association for Computing Machinery.
Communication of the ACM, 48, 10–59.
Kara, A., & DeShields, O. W. (2004). Business student satisfaction, intentions and
retention in higher education: An empirical investigation. Marketing Educator
Quarterly, 3(1), 1–25.
Kuo, Y.-C., Walker, A. E., Belland, B. R., & Schroder, K. E. E. (2013). A predictive study
of student satisfaction in online education programs. International Review of
Research in Open and Distributed Learning, 14(1), 16–39.
Leszczyński, P., Charuta, A., Łaziuk, B., Gałązkowski, R., Wejnarski, A., Roszak, M., &
Kołodziejczak, B. (2018). Multimedia and interactivity in distance learning of
resuscitation guidelines: a randomised controlled trial. Interactive Learning
Environments, 26(2), 151–162.
Liu, G., & Hwang, G. (2010). A key step to understanding paradigm shifts in e‐learning:
towards context‐aware ubiquitous learning. British Journal of Educational
Technology, 41(2), E1–E9.
Eduvest – Journal of Universal Studies
Volume 2 Number 8 , August 2022
1.656 http://eduvest.greenvest.co.id
Muhammad, A., Shaikh, A., Naveed, Q. N., & Qureshi, M. R. N. (2020). Factors affecting
academic integrity in E-learning of Saudi Arabian Universities. An investigation
using Delphi and AHP. Ieee Access, 8, 16259–16268.
Richardson, J. C. (2001). Examining social presence in online courses in relation to
students’ perceived learning and satisfaction. State University of New York at
Albany.
Sáiz-Manzanares, M. C., Marticorena-Sánchez, R., Arnáiz-González, Á., Pastor, J. F. D.,
& Rodríguez-Arribas, S. (2019). Blended learning: an experience with infographics
and virtual laboratories using Self-regulated learning. INTED2019 Proceedings,
2966–2971.
Sandkuhl, K., & Lehmann, H. (2017). Digital transformation in higher education–The role
of enterprise architectures and portals.
Strielkowski, W. (2020). COVID-19 pandemic and the digital revolution in academia and
higher education. Preprints, 1, 1–6.
Swan, K. (2002). Building learning communities in online courses: The importance of
interaction. Education, Communication & Information, 2(1), 23–49.
Tang, M., & Byrne, R. (2007). Regular versus online versus blended: A qualitative
description of the advantages of the electronic modes and a quantitative evaluation.
International Journal on E-Learning, 6(2), 257–266.
Vlachopoulos, D. (2011). COVID-19: Threat or opportunity for online education? Higher
Learning Research Communications, 10(1), 2.