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. 2021 Oct 28;3(5):811–822. doi: 10.1002/hbe2.300

Challenges of online teaching during COVID‐19: An exploratory factor analysis

Mohd Imran Siddiquei 1,✉, Shashank Kathpal 2
PMCID: PMC8653085  PMID: 34901771

Abstract

COVID‐19 has forced academic institutions around the world to shift toward the digital platform for teaching. The study aimed to find a possible model to understand the challenges of online teaching from home. Literature review and expert opinion identified the issues related to students, institutions, instructors, technology, and content, and based on the expert's opinion, the motivation construct was added. To obtain the data faculty members of different universities of North India were approached and requested to fill a self‐administered questionnaire. This data was analyzed for its reliability and validity. Finally, the factor loading of all the items was analyzed to determine the scale appropriability.

Keywords: COVID‐19, exploratory factor analysis, online teaching

Six key factors were recognized and validated using Exploratory Factor Analysis, that were impacting the online teaching during COVID‐19, namely Instructors/Teachers, Institution, Students/Learners, Infrastructure, Content and Motivation.

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1. INTRODUCTION

The spread of the coronavirus epidemic is responsible for the cause of medical emergencies all over the world, due to which millions are suffering from the disease and more than half of the millions have died. The entire world economy has been crushed by COVID‐19. Its spread has caused panic in almost all sectors of the world economy (Ali & Khan, 2020). A nationwide lockdown was announced in India on 22 March 2020 to prevent the epidemic from spreading in the country. Thousands of school colleges and universities have had to be closed due to lockdown in India. Lockdown has affected the education of more than 500 million students in India (Gupta & Tiwari, 2020). Lockdown has forced colleges, universities, and schools all over India to take entire education to the digital platform without any preparations. It has pushed the academic institutions towards online teaching due to the indefinite shutting down of schools, colleges, and universities by the governments (Martinez, 2020). The closure of schools has affected more than 290 million students across the globe (UNESCO, 2020). The education industry has witnessed a pedagogical shift in the teaching toward e‐learning to cope up with the challenges posed by COVID‐19. The issue of students' safety compelled academic institutions to devise strategies for educating students with nominal disruptions (Hale et al., 2020). The teachers were aware of using technology for enhanced learning; however, they were not ready for such revolutionary change.

The education system was obligated to reinvent itself to handle this unparalleled challenge. Earlier the e‐learning was majorly used in nonformal education and distance education courses. However, the COVID‐19 has compelled the formal institutions to embrace e‐learning, as maintaining social distance is of utmost value. Maintaining stringent rules such as maintaining social distancing is improbable in the regular classroom mode of teaching. The motivation of different stakeholders is a prerequisite to breaking the ice of interaction that occurs due to the absence of regular classes (Siegal et al., 1996). Both the teachers and students need to adopt the digital learning process to make e‐learning successful (Lederman, 2020). The sudden change from a brick‐and‐mortar model to only a click model of education and learning has posed serious challenges in front of teachers and students (Adnan & Anwar, 2020; Bdair, 2021). For students in India, the challenges are not limited to technological infrastructure, but it extends to psychological well‐being as well (Pandita et al., 2021). The psychological impact of online teaching among students includes anxiety, and depression (Bashir et al., 2020; Hasan & Bao, 2020; Pandita et al., 2021). The Government of India promoted online teaching to ensure the continuation of learning even during the lockdown. Chinese government emphasized continuous learning even during the suspension of classes in the time of COVID‐19 (Zhang et al., 2020). The emphasis on digital teaching has burdened all the stakeholders of the education industry. A shift toward remote teaching at such short notice globally was a novel phenomenon (Brom et al., 2020). These types of emergencies are not planned and require the competency of using technology to teach remotely (Joshi et al., 2018; Rush et al., 2016). Many teachers are struggling to adopt online teaching (Hodges et al., 2020).

The conventional view suggests that instructors and probably the textbook are the primary source of students' information. Information and Communication Technology(ICT) support, such as audio/videos or slides, or films, is available to teachers. Online teaching is different because the instructor is not there to make students vigilant. The e‐learning system makes the role of the teacher more complex, as teachers have to collect, prepare, and present the information via the internet as a course facilitator (Adedoyin & Soykan, 2020; Bdair, 2021; Buzzetto‐Hollywood, 2007). The complexity of online teaching has increased the workload of teachers many folds (Adnan & Anwar, 2020; Connolly & Begg, 2006).

Post lockdown majority of the educational institutions adopted either synchronous learning or asynchronous learning models. Synchronous learning consists of online or remote training that takes place in real‐time in the form of live sessions using various conferencing software's like zoom, google meet, Blackboard, Skype, and Cisco WebEx, while online learning is achieved without real‐time contact through online networks using WhatsApp, YouTube, and Learning management systems of educational institutes.

Both synchronous and asynchronous models require hardware, software, and network supports for both teachers and students. The recent report from Quacquarelli Symonds (QS) suggested that Internet infrastructure in India is not ready for a complete shift from offline teaching to online teaching (Source: QS I ‐ GUAGE).

Even before COVID‐19 pandemic, developing countries like India were facing challenges and many obstacles in adopting e‐learning (Heeks, 2002). The challenges have become more prominent as the entire education system shifted from offline to online teaching.

There have been numerous studies that have been conducted to understand the quality of e‐learning from different perspectives. Critical questions such as communication, technology, time management, pedagogy, and evaluation have been identified and analyzed through various studies on online teaching (Bassoppo‐Moyo, 2006; Conaway et al., 2005; Ko & Rossen, 2010; Limperos et al., 2015). However, challenges faced by educators in the era of COVID‐19 have been less explored (Adedoyin & Soykan, 2020; Adnan & Anwar, 2020). The objective of this study is to understand the challenges of online teaching from home in the education sector.

2. CONCEPTUALIZING ONLINE TEACHING CHALLENGES

Almost all the major countries have adopted e‐learning in the post‐COVID‐19 scenarios including developing countries like India. The challenges can categorize into three major perspectives, that is, pre‐COVID, during COVID, and post‐COVID. To understand challenges from all perspectives, extensive literature reviews and expert opinions have been used. Numerous studies have highlighted various issues of online education. A large number of studies have suggested various constructs and their measuring elements (Adedoyin & Soykan, 2020; Adnan & Anwar, 2020; Bdair, 2021). While each of these constructs and elements looks important and relevant but it is unclear that which construct and element will be suitable to, measure the challenges of online teaching from home in the era of COVID‐19. Therefore, with the help of a literature review, a list of constructs and items has been prepared (Table 1).

TABLE 1.

Literature review

Factors Description Indicators Sources
Infrastructure A network of computers connected by transmission medium and software. Internet servers, web servers, Internet storage, Internet network equipment, and infrastructure software are all included in this category.

Electricity

Computers

Internet

Maintenance of infrastructure network

 Dhanarajan (2001); Rajesh (2003) Ssekakubo et al. (2011); Tarus et al. (2015); Ko and Rossen (2010); Limperos et al. (2015); Aczel et al. (2008); Gulati (2008); Islam and Doyle (2008)
Students/ Learners The online learner is an individual taking education over the internet.

Interaction

Participation

Expectations

Readiness

Learning style

Technical skills

Isolation

Training

 Eastmond (2000); Evans (2005); Sehrt (2003); Abdon et al. (2007); Keller and Suzuki (2004); Melinda de la Pena (2007); Siritongthaworn et al. (2006); Luyt (2013); Koole (2014); Ching and Hsu (2015); Makokha and Mutisya (2016); Al‐Azawei et al. (2016); Zawacki‐Richter and Naidu (2016)
Teacher/Instructor Online teachers are separated from learners in location, time, and distance. They use Internet technology to deliver lectures

Role of faculty

Transition communication barriers

Faculty interest

Preparation

Teaching styles

Time

Training

 Abdon et al. (2007); Keller and Suzuki (2004) Melinda de la Pena (2007); Siritongthaworn et al. (2006); Neely and Tucker (2010); Syverson and Slatin (2010); Juan et al. (2011); Tunks (2012); Al‐Azawei et al. (2016)
Content Combination of text, images, music, videos that help in delivering online lectures.

Content

Multimedia

Content development

Content innovation

Pedagogy

 Safavi (2008); Rahman (2006); Melinda de la Pena (2007); Islam and Doyle (2008); (Dhanarajan (2001); Fozdar et al. (2006); Mayer (2014); Miller (2014); Luyt (2013); Tarus et al. ( 2015)
Institutional factors Combination of factors influenced by organizational policy and culture

Policies incentives

Training resources.

 Safavi (2008); Rahman (2006); Czerniewicz and Brown (2009); Ocak (2011); Mahmoodi‐Shahrebabaki (2014); Gutiérrez‐Santiuste and Gallego‐Arrufat (2016); (Zawacki‐Richter & Naidu, 2016)
Motivational factors “The attribute that moves us to do or not to do something” (Broussard & Garrison, 2004)

Salary

Job security

Family support

Mental and emotional support

 Expert opinion
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The literature contributed five important factors like instructors, institutions, learners, infrastructure, content factors that are crucial as challenges of teaching online. The factor of motivation, which is recommended by the experts, can be viewed from a different point of view including the role of the instructor, the process of learning, the environment of learning, and the role of participants. Researchers have looked at motivational variables as significant indicators of desire to continue with any form of technology‐assisted education (Panisoara et al., 2020). Since the target sample for this study is faculty members working in private universities, motivation factors become more imminent from the Indian perspective as these institutions are known for their exploitative policies (Sindhi, 2012). We took those factors that affect the motivation of faculty members which are salary, job security, family support, mental, and emotional support. A paucity of research was found which could incorporate the challenges that emerge out of the present pandemic in the life of a faculty, to adopt online teaching. Therefore, to fill the existing research gap, the study will propose the model to understand the challenges faced in online teaching during COVID‐19.

3. METHODOLOGY

The proposed model “Challenges of Online Teaching during‐COVID 19” has been designed to understand problems faced by instructors/teachers in the delivery of online lectures to the students in the COVID‐19 scenario. The following nine steps were undertaken to propose the model (Figure 1).

  1. Literature review: Review of literature is one of the prominent and effective ways to find out the factors and measures used in determining any phenomenon (Knopf, 2006). The first step of conceptual definitions, their elements, and description started with a literature review of existing studies in the online teaching domain. More than 50 studies were reviewed to understand conceptual definitions, that can be a theoretical base for model development

  2. Development of list of items: A total of 30 elements were identified and grouped under instructors, institution, learners, infrastructure, and content factors. The factors selected for the study represent the challenges in the adoption of online teaching (Adedoyin & Soykan, 2020; Adnan & Anwar, 2020; Bdair, 2021).

  3. Expert opinion: List of elements and their broad factors were presented to five academicians working in the domain of engineering and management (two professors and three associate professors) for evaluation of the relevance of each item, reclassify the items, point out ambiguity, and suggest a replacement or additional items (Devellis, 1991). Experts have suggested a new factor as motivation with five items including salary, job security, family support, mental, and emotional support to incorporate challenges that arise due to COVID‐19. Some minor changes have also been suggested by experts like network speed and online counseling sessions for teachers, which are peculiar to Indian Internet infrastructure and stress‐relieving exercises for teachers.

  4. Questionnaire development with a final list of items: The factors used to examine the challenges of online teaching were finalized after the extensive review of the existing literature and with the help of experts in the field (Bohrnstedt, 1983). A total of six factors (instructors, institutions, students/learners, infrastructure, content factors, and motivational factors) with five items each were constructed for the final development of a questionnaire. All the items were measure on a 5‐point Likert scale ranging from “Strongly disagree To Strongly Agree.” The questionnaire was also verified with the experts of the field who helped in finalizing factors (Caro & Garcia, 2007).

  5. Face and content validity: Content validity of a questionnaire comes from the review of literature while face validity could be achieved by considering expert opinion (Taherdoost, 2016). All the items were taken from the extensive literature review and validated by experts; therefore, achieving both content and face validity (Bohrnstedt, 1983; Caro & Garcia, 2007).

  6. Data collection: Due to COVID‐19, it was not feasible to collect data by sending physical questionnaires. Therefore, the questionnaire was designed in a google form and sent through email and WhatsApp to 300 faculty members working in the private universities of north India. Gentle reminders were sent at regular intervals to the respondents. Total 124 responses were received; nine responses were dropped due to incompletion and 115 were taken as the final sample size.

  7. Reliability, validity, and normality: To analyze the internal consistency and strength of the results the reliability of the instrument is measured (McMillan, 2007). The reliability was tested with the help of Cronbach's alpha. To analyze whether the data is adequate to run for Exploratory Factor Analysis (EFA), the Kaiser‐Meyer‐Olkin (KMO) test was run (Marsh et al., 2009). The normality was also tested by analyzing the skewness of data before running EFA.

  8. Factors determination: The eigenvalues of the 30 items were calculated to determine the number of factors to be considered for the study. A scree plot is also created for the same.

  9. Exploratory factor analysis: To have a reliable scale, the items selected must be suitable. With the help of EFA, inappropriate items can be removed. The EFA is a very useful tool to categorize the constructs when there is a paucity of information available on their dimensionality (Netemeyer et al., 2003). The scale for the proposed model was analyzed with the help of EFA using the software of SPSS 20. A total number of 30 items were finalized to represent the six latent variables used to run the EFA. A sample of 115 respondents is taken, which is considered to be suitable to conduct EFA (Churchill Jr, 1979; Hair et al., 2010; Malhotra & Das, 2010). The EFA analysis was conducted over Principal Component Analysis (PCA) using the Varimax rotation while analyzing the principal component.

FIGURE 1.

FIGURE 1

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Scale development methodology

4. ANALYSIS

The current study aims to investigate the suitability of the proposed model as well as the items included in it. The data collected were analyzed using SPSS 20 has indicated the following findings:

4.1. Normality

The normality of the items is confirmed by analyzing the skewness of the data which is created using SPSS 20. If the degree of skewness is less than the absolute 1, the data is considered to be normally distributed (Field, 2009). Table 2 shows that the data used in the study is normally distributed.

TABLE 2.

Descriptive analysis

N Minimum Maximum Mean SD Skewness
Statistic Statistic Statistic Statistic Statistic Statistic SE
Student 115 1 5 3.63 .811 −.720 .226
Institution 115 1 5 3.05 1.255 −.127 .226
Instructor 115 2 5 4.09 .812 −.862 .226
Content 115 2 5 4.23 .798 −.977 .226
Motivation 115 1 5 3.64 1.036 −.633 .226
Technology 115 1 5 3.98 .946 −.788 .226
Valid N (listwise) 115
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4.2. Reliability

If the value of Cronbach's alpha is higher than 0.8, it is considered to have very good internal consistency (Blunch, 2008), while Nunnally (1994), considers the value of 0.7 as reliable. The outcome values of Cronbach's alpha for the current study ranges from 0.886 to 0.936, indicating good reliability of the data (Table 3).

TABLE 3.

Cronbach's alpha

Construct Value of Cronbach's alpha
Students 0.893
Institution support 0.909
Instructors 0.904
Content 0.900
Motivational support 0.886
Technological Factors 0.936
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4.3. KMO and Bartlett's test

The acceptable value for the KMO test should be greater than 0.6 (Hair et al., 2010; Kaiser, 1974; Tabachnick & Fidell, 2013). The result of KMO analysis was 0.880 (Table 4), which is an acceptable value to run PCA in EFA. The result for Bartlett's Test was having a p‐value of .00, which is also considered significant.

TABLE 4.

Kaiser–Meyer–Olkin (KMO) and Bartlett's test

KMO value 0.88
Approx. Chi‐square 2819.23
Bartlett's test 435
Degrees of freedom 435
Significance 0.000
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4.4. Eigen value and scree plot

The total variance of each latent variable is characterized by eigenvalue. According to Kaiser (1960), factors with an eigenvalue of more than 1 is should be considered for retention. The eigenvalue for six factors was found to be greater than 1 (Table 5) and the same is confirmed by using a scree plot (Figure 2). While 24 variables did not qualified to be considered as factors, because their eigenvalue is less than 1 (DeVellis, 2003). The same results can be seen in the scree test, where the six factors are seen on a vertical slope (Figure 2) and hence considered for the retention (Comrey & Lee, 1992; DeVellis, 2003).

TABLE 5.

Total variance

Component Initial eigenvalues a
Total Percentage of variance Cumulative percentage
1 11.191 37.304 37.304
2 3.78 12.601 49.904
3 2.885 9.616 59.521
4 1.917 6.391 65.911
5 1.749 5.831 71.742
6 1.391 4.636 76.378
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a

Extraction Method Principal Component Analysis

FIGURE 2.

FIGURE 2

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Scree plot

4.5. Exploratory factor analysis

EFA is used to develop new instruments. Comrey and Lee (1992) suggested that if the factor loading is more than 0.63, it is considered to be good. As shown in Tables 6 and 7, all of the 30 items of the given five constructs have shown a very good factor loading value ranging from .662 to .858, which reflects a robust correlation between variables and factors. Due to the outstanding results of EFA, all the items were retained and none were removed.

TABLE 6.

Rotated component matrix a

Component
1 2 3 4 5 6
Content 3 .820
Content 1 .800
Content 2 .783
Content 4 .777
Content 5 .734
Institution 3 .844
Institution 5 .843
Institution 2 .829
Institution 1 .821
Institution4 .780
Technology 2 .858
Technology 1 .820
Technology 3 .798
Technology 5 .749
Technology 4 .727
Motivation 3 .855
Motivation 2 .819
Motivation 4 .784
Motivation 1 .783
Motivation5 .775
Student 1 .882
Student 4 .860
Student 2 .825
Student 3 .726
Student 5 .695
Instructor3 .830
Instructor 1 .808
Instructor 2 .778
Instructor 5 .700
Instructor 4 .662
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Notes: Extraction method: Principal component analysis. Rotation method: Varimax.

a

Rotation converged in six iterations.

TABLE 7.

Factors along with their attributes

Factors along with their attributes Factor loading
Students Cronbach's Alpha: .893
My students are ready to learn online. .882
My students have the technical skills to learn online. .825
My students do not have network and speed issues. .726
My students do not hide identity in online learning. .860
My students are interactive and participate in my class .695
Institution support Cronbach's alpha: .909
My institution has given me adequate professional training for online teaching. .821
My institution has provided adequate training to students for online learning .829
My Institution provided me technical and multimedia support .844
My institution has a dedicated Team for troubleshooting that arises in an online class. .780
My institution has arranged online counseling sessions for me. .843
Instructors Cronbach's Alpha: 0.904
I am as comfortable teaching online as offline. .808
I do have not any communication barriers in teaching online. .778
I am interested in online teaching. .830
I am well prepared before taking an online class. .662
I have changed my teaching style for online teaching. .700
Content Cronbach's alpha: .900
I have developed a new material for online teaching. .800
I am using multimedia tools (Videos, PPT, and Animation) for online teaching. .783
I am giving regular assignments to students. .820
I am checking assignments regularly given to the students. .777
I am taking regular feedback from students of online teaching .734
Motivational support Cronbach's alpha: .886
I am getting my salary on time. .783
I am having a sense of job security. .819
I am getting family support. .855
I am getting mental and emotional support from colleagues. .784
I am getting mental and emotional support from higher authorities. .775
Technological factors Cronbach's alpha: .936
My network connection is stable. .820
My network connection is having adequate speed. .858
My device (mobile/laptop) is suitable for online teaching. .798
My video conferencing software (Zoom, Google meet, Skype, Blackboard) is easy to use. .727
My video conferencing software (Zoom, Google meet, Skype, Blackboard) are having adequate tools for online teaching. .749
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5. DISCUSSIONS

This study aims to analyze the challenges faced by the academicians in shift their teaching pedagogy from traditional to online mode due to COVID‐19. This paper provides different perspectives pertaining to the adoption of online teaching while remaining within the boundaries of existing literature. The results present novel insights in understanding the perception of faculty members toward the adoption of online education.

This pandemic has given many lessons to every industry including education. An important takeaway for education institutes is to be prepared for effective digital learning.

The findings of the study indicate the relevance of technological infrastructure and commitment from the top management in making e‐learning effective. The results also emphasized the role of employees' as well as students' motivation in making digital learning valuable. During the emergencies such as COVID‐19, remote teaching could be proven a feasible substitute for traditional teaching. However, its effectiveness could only be studied in the future by analyzing the performance of students. The performance of students could deteriorate due to the challenges posed by this new method of education. In the end, it is essential to address the challenges posed in the adoption of digital learning to make it effective.

Literature indicates a poor ability of faculties in using digital platforms for teaching (Foulger et al., 2017). The faculties who were not versed with online teaching faced enormous challenges, as they need to learn teaching online while delivering the lectures online. Despite the difficulties, the digital platform for teaching provides opportunities to faculties for experimental teaching, as teaching in different setups and platforms was possible (Archambault & Kennedy, 2014; Graham et al., 2019; Pulham & Graham, 2018; Zweig & Stafford, 2016). The faculty members were motivated to support the shift toward e‐learning (Trust et al., 2016).

6. CONCLUSION

A review of the literature, expert opinion, and EFA were performed to understand the major challenges posed by COVID‐19 in teaching online from home.

There are six major factors identified as instructors, institution, students/learners, infrastructure, content factors, and motivational factors for the proposed model (Figure 3). Challenges related to instructors included a transition to online from offline, communication barriers innate in online teaching, preparation, teaching style. Challenges related to institutions include training for teachers and students, technical and multimedia support, technical troubleshooting team, online counseling sessions for teachers. Challenges related to students are readiness, technical skills to learn online, network and speed issues, identity, interaction, and participation. Challenges related to content include the development of new material, multimedia tools (Videos, PPT, and Animation) regular assignments, checking assignments, regular feedback from students. Challenges related to motivational factors include salary on time. Sense of job security, family support, mental and emotional support from colleagues and higher authorities. Challenges related to technological factors include network stability and speed, device suitability, ease of use, and tools of conferencing software for online teaching.

FIGURE 3.

FIGURE 3

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Proposed model: Challenges Of Online Teaching During COVID‐19

7. CONTRIBUTION AND PRACTICAL IMPLICATIONS

This study will bridge the gap of existing literature on major challenges of online teaching from home in post‐COVID‐19 scenarios. As social distancing has become the norm, this study will help higher institutions to understand major challenges and try to come up with strategies for a better online learning environment for students and faculty members by focusing on factors that can be solved at an institutional level like counseling, adequate training, job security. The study will also help the government to focus on a critical area like Indian Internet infrastructure which plays an important role in the success of online teaching and learning in India. Finally, it helps faculty members as they are responsible for service delivery. They can employ innovative teaching strategies to improve students' participation and interaction in online classes.

7.1. Limitations and future research implications

The study is not without any limitations. The study was conducted in the Indian context; therefore, generalization for other countries is limited. The study employs a small sample size due to limitations of accessibility to respondents due to COVID‐19; therefore future can explore factors with greater sample sizes. Future research must try confirmatory factor analysis to validate the proposed model further. The study can be replicated in other developing countries due to the existence of a similar type of challenge, which can further improve the acceptability of the proposed model.

CONFLICT OF INTEREST

The author states that there is no conflict of Interest.

PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1002/hbe2.300.

Biographies

Mohd Imran Siddiquei is working as an Assistant professor with the University of the People. He has completed his PhD from Aligarh Muslim University, Aligarh in Business Administration with a core area of Service quality. He has 14 years of experience in teaching Management subjects like Marketing Management, Research Methodology, Marketing of Services, International Business, Managerial Economics, Financial Services, Strategic Management, and Brand Management. As an avid researcher, He has many Scopus Indexed and ABDC Category Papers. He has covered Research areas like Service quality, Service delivery, Consumer Ethics, Brand Loyalty, Vendor Selection, Consumers' Frequency of Eating Out, Public Policy, and many papers on Challenges of COVID‐19 in various sectors of an Economy. His future research aspirations involve a special focus On COVID‐19 and emerging challenges in Hospitality, Education, Banking, transport, tourism, and many other services sectors. He is also interested in collaborative research projects with researchers, students, community organizations.

biography image

Shashank Kathpal is currently working as an assistant professor at the Institute of Business Management, GLA University, Mathura, UP. His area of specialization includes Marketing and Analytics. He has completed three post graduations whose details are as follow: Masters in Commerce from Guru Jambheshwar University, Haryana, India, Post Graduate Diploma in Management from Kirloskar Institute of Advanced Management Studies, Karnataka, India, MSc in Applied Psychology: The Global Open University, Nagaland, India. He is pursuing a PhD in (Business Administration) from Aligarh Muslim University, Aligarh India, and the topic of His doctoral work is “Investigating the relationship between personality traits and investors' bias: A study of financial literates”. He has also earned multiple certifications in Analytics through Massive open online courses and attended many Faculty Development programs at reputed institutes. He has more than 8.5 years of teaching experience. He has published many Scopus indexed papers and actively working on future research output.

biography image

APPENDIX A. The questionnaire provided in the Appendix are developed by the authors

Dear Faculty,

As we know that Covid‐19 has forced all the leading institutions to move their classes online. We are conducting an online survey about the challenges faced by the faculty pertaining to adoption of online teaching. Please fill the form with utmost honesty. It should take approximate 10 min only.

We assure you that the information given by you will be used for academic purpose only.

Section‐A (Demographic Profile)

Name: _______________________________.

Email: ________________________________.

Gender:

  1. Male

  2. Female

  3. Prefer not say

Educational Institute________________________.

Monthly Income (In Rs.):

  1. Less than 20,000

  2. 20,000–35,000

  3. 35,000–50,000

  4. More than 50,000

Education Qualification:

  1. Graduate

  2. Post Graduate

  3. PhD/FPM

  4. PDF/Dlitt

Section‐B (Educator's Perception)

Kindly rate all of the following statement from 1 to 5, where:

1: Strongly disagree; 2: Disagree; 3: Neither agree nor disagree; 4: Agree; 5: Strongly agree

Students

  1. My students are ready to learn online.

  2. My students have technical skills to learn online.

  3. My students do not have network and speed issues.

  4. My students do not hide identity in online learning.

  5. My students are interactive and participate in my class.

Institution support

  1. My institution has given me adequate professional training for online teaching.

  2. My institution has provided adequate training to students for online learning

  3. My Institution provided me technical and multimedia support

  4. My institution has a dedicated Team for troubleshooting arises in online class.

  5. My institution has arranged online counseling sessions for me.

Instructors

  1. I am as comfortable teaching online as offline.

  2. I do have not any communication barriers in teaching online.

  3. I am interested in online teaching.

  4. I am well prepared before taking online class

  5. I have changed my teaching style for online teaching.

Content

  1. I have developed new material for online teaching.

  2. I am using multimedia tools (Videos, PPT, Animation) for online teaching.

  3. I am giving regular assignments to students.

  4. I am checking assignments regularly given to student's

  5. I am taking regular feedback from students of online teaching.

Motivational support

  1. I am getting my salary on time.

  2. I am having a sense of job security.

  3. I am getting family support.

  4. I am getting mental and emotional support from colleagues

  5. I am getting mental and emotional support from higher authorities.

Technological Factors

  1. My network connection is stable.

  2. My network connection is having adequate speed.

  3. My device (mobile/laptop) are suitable for online teaching.

  4. My video conferencing software (Zoom, Google meet, Skype, Blackboard) is easy to use.

  5. My video conferencing software (Zoom, Google meet, Skype, Blackboard) are having adequate tools for online teaching.

Source: Prepared By the Authors.

Siddiquei, M. I. , & Kathpal, S. (2021). Challenges of online teaching during COVID‐19: An exploratory factor analysis. Human Behavior and Emerging Technologies, 3(5), 811–822. 10.1002/hbe2.300

Funding informationThere are no funders to report for this submission.

DATA AVAILABILITY STATEMENT

Data available on request from the authors

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