




































BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 9(2) (2024), 10-18 

10 

        MULTIDISCIPLINARY SCIENTIFIC RESEARCH 
          BJMSR VOL 9 NO 2 (2024) P-ISSN 2687-850X E-ISSN 2687-8518 

         Available online at https://www.cribfb.com 

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 
                                                                                                                                     

USER ATTITUDE TOWARDS PREFERENCE OF E-LEARNING IN 

BANGLADESH                     
 

Mustafa Manir Chowdhury (a)   Md. Shahidul Islam (b)  Mohammad Toufiqur Rahman (c)  Kulsuma Akter (d)               
 A. M. Shahabuddin (e)1       

 

(a)Associate Professor, Department of Business Administration, International Islamic University Chittagong, Bangladesh; E-mail: 

mustafa_manir@iiuc.ac.bd 
(b)Divisional Officer, Service Engineering Division, Bangladesh Forest Research Institute, Bangladesh; E-mail: 

engr.shahidul.islam@gmail.com    
(c)Associate Professor, Department of Business Administration, International Islamic University Chittagong, Bangladesh; E-mail: 

toufiq_robin@yahoo.com 
 (d)Assistant Professor, Department of Business Administration, International Islamic University Chittagong, Bangladesh; E-mail: 

nahidkulsuma@iiuc.ac.bd 
 (e)Associate Professor, Department of Business Administration, International Islamic University Chittagong, Bangladesh; E-mail: 

ams_iiuc@yahoo.com 
                     

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 31st March 2024 

Reviewed & Revised: 1st April  

to 12th June 2024 

Accepted: 15th June 2024 

Published: 21st June 2024 

 
Keywords: 

 

E-Learning, User Attitude,  

Preferences, Awareness, Cost 

 
JEL Classification Codes: 

 

D83 

   

 
      Peer-Review Model:  

 

      External peer-review was done through  

      double-blind method.        

 
A B S T R A C T 

 
Information and Communication Technology (ICT) is crucial in facilitating the teaching and learning 
process in the current educational setting during COVID-19. In this context, the global demand for the 

integration of ICT in Education has rapidly increased. During the COVID-19 pandemic, e-learning 

gained widespread popularity as people remained indoors for health and safety reasons. This situation 

led to a rapid increase in positive attitudes toward e-learning. This study investigates the factors 

influencing students' attitudes toward e-learning and examines whether this Attitude impacts their 

Preference for e-learning. Utilizing survey data from 400 respondents collected between January and 

March 2024, this research employs a purposive sampling approach with questionnaires measuring 
awareness of e-learning, e-learning education facilities, benefits, costs, attitudes, and preferences, all 

on a five-point Likert scale. Factor analysis categorizes the data into six factors, which are then validated 

using Cronbach's alpha in IBM SPSS Statistics 26. A structural equation model (SEM) is developed in 

IBM SPSS AMOS 22 to analyze the Preference for e-learning education based on attitudes and their 

associated factors, further validated through convergent and discriminant validity. The results reveal 

that e-learning facilities and benefits positively influence attitudes towards e-learning, while the Cost of 

e-learning negatively impacts these attitudes. However, awareness of e-learning only significantly 

affects attitudes. A positive attitude towards e-learning significantly enhances the Preference for e-
learning education. The findings of this study highlight the critical role of perceived benefits and cost 

considerations in shaping student attitudes and preferences for e-learning education. 

 
 

© 2024 by the authors. Licensee CRIBFB, USA. This open-access article is distributed under the 
terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0).  

            

 

INTRODUCTION 

The education system, which traditionally relied on the triangular relationship between school, instructor, and student, has 

increasingly embraced new, diverse, and multi-channel options facilitated by technological advancements. One of them is 

referred to as "online learning". Online Education is a flexible means of delivering learning materials encompassing all 

online learning types. Online learning allows instructors to engage with students who cannot partake in a conventional 

classroom curriculum and assist students who want to study alone and at their own pace. The prevalence of distance learning 

and online degrees is substantial, and they are experiencing tremendous growth across several disciplines. Furthermore, 

there is a burgeoning proliferation of educational establishments and organizations that provide opportunities for remote 

learning through online platforms. Students pursuing online degrees must exercise caution to verify their coursework is 

completed through a recognized and certified institution. Amidst the COVID-19 pandemic, Information and Communication 

Technology (ICT) is crucial in facilitating the teaching and learning process in the current educational setting. Hence, it is 

now essential to integrate ICT into schooling. The global demand for online courses through the integration of ICT in 

                                                      
1Corresponding author: ORCID ID: 0000-0002-2692-3634 
© 2024 by the authors. Hosting by CRIBFB. Peer review is the responsibility of CRIBFB, USA.  

https://doi.org/10.46281/bjmsr.v9i2.2217 

 
To cite this article: Chowdhury, M. M., Islam, M. S., Rahman, M. T., Akter, K., & Shahabuddin, A. M. (2024). USER ATTITUDE TOWARDS 

PREFERENCE OF E-LEARNING IN BANGLADESH. Bangladesh Journal of Multidisciplinary Scientific Research, 9(2), 10-18. 

https://doi.org/10.46281/bjmsr.v9i2.2217 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://www.openaccess.nl/en
https://doi.org/10.46281/bjmsr.v9i2.2217
https://orcid.org/0000-0002-8085-9864
https://orcid.org/0000-0001-5755-2365
https://orcid.org/0000-0003-3494-0861
https://orcid.org/0000-0001-9232-9740
https://orcid.org/0000-0001-6469-1873


Chowdhury et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(2) (2024), 10-18

 

11 

Education has rapidly increased in this context. The international landscape has continuously transformed since the onset of 

the COVID-19 (Coronavirus) epidemic in Wuhan, China. Over time, COVID-19 began to impact countries worldwide, and 

on 08 March 2020, the virus was detected in Bangladesh, causing widespread fear among the population. Despite initial 

concerns from educational institutions on the continuity of academic activities during the epidemic, the education sector has 

gradually transitioned to an internet platform. Around 70% of graduate and undergraduate students actively engage in online 

teaching and learning, most of whom use smartphones. Online Education has gained significant traction in industrialized 

nations, with professors and students embracing it as a credible form of Education. This is mainly attributed to the 

exceptional instructional standards and the accessibility offered by online platforms. Insufficient resources and many 

constraints make it challenging for students in underdeveloped nations like Bangladesh to adjust to online Education, which 

is still a relatively new type of instruction. Following the COVID-19 pandemic, the Bangladesh government implemented 

many measures to facilitate the widespread adoption of online Education across all levels of the education system. Due to 

its recent implementation in Bangladesh, students face several challenges in Online Education, such as limited technical 

support, insufficient access to devices and the Internet, bad network connectivity, and expensive Internet packages. 

Consequently, these obstacles impact students' motivation to engage in online learning. A significant number of studies have 

been conducted on the attitudes of instructors. However, more research needs to be done on students' Attitudes to online 

learning in Bangladesh, spanning primary, secondary, and higher education levels. Therefore, the research aims to fill in the 

knowledge gap. As the concept is new in the context of Bangladesh, it requires a rigorous study, keeping in mind the 

demands of the stakeholders. E-learning requires many issues to be settled to gain full benefits. The facility gaps necessary 

to be consulted in the studies we consulted are to be identified. So, there is a research gap that needs to be minimized to 

make e-learning effective by mitigating the demands of the users and developing the infrastructure. An empirical study is 

undertaken to reduce the gap that may be utilized in policy decisions and implementation to popularise e-learning education 

in the digitalization of smart Bangladesh. The study aims to help formulate educational policy by determining the factors 

affecting the user's (student) attitude toward e-learning. Additionally, whether this Attitude plays any role in the Preference 

for e-learning has also been tested (Faisal-E-Alam, 2024).  

The remainder of the paper is structured as follows. The second section provides a brief literature review and provides 

the hypotheses, while the data and methods are defined in the third section. The fourth section analyses the findings and 

presents the discussion. In the final section, the conclusion includes the key findings, managerial implications and drawbacks 

of the study, and recommended lines for future inquiry. 

 

LITERATURE REVIEW 

Sanders and Morrison-Shetlar (2001) investigated undergraduate students' perceptions of a biology course's web-based 

learning elements. The results influenced students' critical thinking abilities, problem-solving aptitude, and learning 

outcomes. Rhema and Miliszewska (2014) examined the viewpoints and observations of students on the use of technology 

for learning at two universities in Libya. The study examined the impact of demographic variables, technological exposure, 

and proficiency in using learning technology, technical abilities, and contentment with technology on students' attitudes. 

The results indicated that demographic factors, including student locality, gender discrepancies, current year of enrolment, 

and age, did not influence students' attitudes toward e-learning. Students who had been exposed to technology were more 

inclined towards e-learning. Students' technological proficiency has a significant role in shaping their attitudes towards e-

learning.  

Teachers have a pivotal role in educational settings, and their comprehension of e-learning impacts students' 

disposition towards e-learning. Krishnakumar and Rajesh (2011) evaluated the disposition of higher education instructors 

towards e-learning. The study findings indicated a cheerful disposition. There were differences in the approach between 

technologically proficient instructors and technologically unskilled teachers. Alsaaty et al. (2016) examined business 

students' perceptions of conventional and online higher education learning. The survey revealed that 30.2% of students find 

the materials provided in online classrooms to be accessible, simple to use, and comprehend. Conversely, 69.2% of students 

expressed discomfort with the online materials due to their perceived lack of ease. 

Yang and Cornelius (2004) conducted a qualitative research study with three participants. Their objective was to 

identify the determinants for assuring the quality of online teaching and learning in higher Education. Both positive and 

negative feedback were identified throughout their investigation. The students expressed their positive experiences 

regarding flexibility, cost-effectiveness, access to electrical research, ease of internet connectivity, and well-designed class 

interface. Conversely, the negative feedback included delayed instructor feedback, unresponsive technical support, 

insufficient self-regulation and motivation, feelings of isolation, unengaging instructional methods, and inadequately 

designed course content. 

Islam et al. (2021) studied the perspective of tertiary-level students in Bangladesh toward online classrooms during 

the COVID-19 epidemic. They aimed to enhance the quality and acceptance of online classes in the future. The assessment 

of students' perceptions has been conducted across many categories. Using the snowball sampling approach, six hundred 

seventy-seven students participated in the online survey. The study revealed that 84.19% of the participants were enrolled 

in private universities, and 75.2% utilized smartphones to attend online classes. Approximately 38.8% of students rely on 

expensive cellphone data. 

In his qualitative study, Naomee (2023) examines the perspectives of University of Dhaka students on this novel 

approach to Education and its impact on their lives. The discovery indicates that the primary difficulties pertain to 

infrastructural circumstances, economic circumstances, mental wellness, and the interaction between teachers and students. 

This study also examines potential resolutions for these obstacles and methods to integrate online or blended education 

systems into the higher education system of Bangladesh. Rahman et al. (2023) conducted a study on the perspective of 



Chowdhury et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(2) (2024), 10-18

 

12 

online Education among higher secondary students in Bangladesh during the COVID-19 pandemic. The objective is to 

discover the elements that influence their attitudes. The research is quantitative. Data from 1078 students were collected via 

an online survey employing a multistage random sampling method. The findings indicate that three key aspects, namely the 

obstacles and complexities associated with online learning, the efficacy of online Education, and students' motivation, 

significantly influence students' perceptions of online learning. 

The data obtained from 699 participants were analyzed using the SPSS 23 program. The study's findings will enable 

educational institutions and policymakers to advance the online learning process in a forward-looking manner—the survey 

conducted by Haque (2023) aims to determine the instructors' attitudes toward online classrooms and the problems they 

encountered while giving these sessions. The inquiry utilized a mixed-method approach, collecting data through surveys 

and in-depth interviews. The research findings indicate unanimous agreement among teachers on the indispensability of 

online classes, particularly in a pandemic such as COVID-19. 

Sarkar et al. (2021) researched to investigate the opinions of public university students in Bangladesh about online 

classes during the COVID-19 epidemic. An online survey was conducted to gather data from students at Islamic University, 

Kushtia, Bangladesh. The study employed a quantitative methodology, utilizing the survey technique as a means of data 

collection. The findings indicated that most students had challenges actively engaging in virtual classrooms and experienced 

difficulties communicating effectively with their peers during online sessions. Gurung et al. (2022) conducted a study to 

examine the elements and beliefs that impact instructors' inclination to utilize online classroom software for online teaching. 

The study employed a descriptive cross-sectional survey involving 227 instructors teaching online throughout the pandemic. 

The results indicated a favorable view of teaching online through online platforms. The study results suggest that elements 

such as training and administrative support, trust, digital literacy, online teaching competence, and perceived security 

significantly impact the desire to utilize online classroom applications. 

Afroz et al. (2021) conducted an assessment of the attitudes of students and instructors about transitioning to a 

fully online learning environment in response to COVID-19. The primary aim of the study was to examine the perspectives 

of students and teachers on online Education in Bangladeshi Government Colleges during the COVID-19 pandemic. The 

findings indicated that the online learning experience was frequently praised for its Cost and time-effectiveness, safety, 

convenience, and improved participation. However, it was also commonly criticized for causing distraction. It reduced 

focus, having a heavy workload, encountering problems with technology and the Internet, lacking ICT knowledge, facing 

poor network infrastructure, having limited availability of educational resources, experiencing low attendance of learners, 

dealing with uncooperative learners, and receiving insufficient support from instructors and colleagues.  

The study conducted by Ahmed et al. (2022) investigated the Attitude towards Online Teacher Education Courses 

(OTEC). This survey was conducted among the teacher educators and trainee teachers of Bangladesh Open University. The 

study employed a mixed research strategy, utilizing triangulation, which involved conducting a descriptive survey, two 

focus group discussions (FGDs), and one key person’s interview. A total of 602 teacher trainees (182 M.Ed. students and 

420 B.Ed. students) and 12 teacher educators were included in the study. The participants were recruited using diverse 

sampling procedures. This research enhances the existing knowledge base by conducting an exploratory survey of all 

relevant stakeholders throughout the nation of interest. 

The study aims to determine the factors that affect the user's (student) attitude toward e-learning. Another study 

objective is to determine whether this Attitude plays a vital role in the Preference for e-learning. The following hypotheses 

have been developed based on the literature review and the above discussion. Then the conceptual model of the study is 

depicted in figure 1. 

Hypothesis H1: User awareness substantially affects user’s e-learning attitude.  

Hypothesis H2: Available e-learning facilities lead to forming a positive e-learning attitude. 

Hypothesis H3: Users' perceived benefits positively affect their e-learning attitude. 

Hypothesis H4: User cost hurts e-Learning attitude. 

Hypothesis H5: There is a positive effect of e-learning attitude on Preference for e-learning. 

 

Conceptual Framework 

 

 

 

 

                     Hy       H   

 

 

 

 

 

 

 

 

 

Figure 1. Conceptual Model 

 

 

E-learning 

Awareness 

E-learning 

Facilities 

E-learning 

Benefit 

E-learning 

Cost 

E-learning                

Attitude 

E-learning 

Preference 

H1 

H2 

H3 

H4 

H5 



Chowdhury et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(2) (2024), 10-18

 

13 

MATERIALS AND METHODS 

An exploratory approach was followed to determine the Preference for E-learning by collecting data from the major 

stakeholders. The methodology mediates between research questions and data and relates the collected data to the desired 

outputs. The research was conducted by collecting primary data. Data were coded and analyzed using statistical tools after 

collecting data from the respondents. Sampling is a procedure in which a small number of items from a large number are 

chosen. A sample is a subset or a piece of a population that has been selected. The goal of employing sampling is to make 

it easier for a small number of people to estimate some known features of a population.  

The survey questionnaire was pre-tested with 20 experienced respondents. After necessary modifications and 

corrections, the survey questionnaire (Appendix A) was distributed to 500 respondents selected by a purposive sampling 

approach. From the collected response data, 400 responses were finally selected (which covers a confidence level of 95%, 

margin of error of 5%, and population proportion of 50% with unlimited population size), as some respondents answered 

all the questions the same rank and did not answer many questions. Among the valid respondents, 262 (65.5%) are male, 

and 138 (34.5%) are female respondents, from which 142 (35.5%) studied in Business Administration faculty, 104 (26.0%) 

studied in Science and Engineering faculty, 90 (22.5%) studied in Social Science faculty and 64 (16.0%) studied in Law 

faculty. The collected response questionnaire data are classified into six categories using factor analysis: user (student) 

Attitude of e-learning, e-learning awareness, e-learning facility, e-learning benefit, e-learning cost, and Preference of e-

learning education and validated by Cronbach's alpha (IBM SPSS Statistics 26). Based on the above classification, a 

structure equation model (SEM) is developed in IBM SPSS AMOS 22 for the Preference of e-learning education from its 

Attitude of e-learning with its factors and validated with convergent and discriminant validity. Finally, all the hypotheses 

are tested, and the necessary conclusion is reached. 

 

RESULTS AND DISCUSSIONS 

Descriptive Statistics of Respondents 

The descriptive statistics (N, Min, Max, Median) and normality test (Kolmogorov– Smirnov Test and Shapiro–Wilk Test) 

of the respondent values for awareness toward e-Learning, e-Learning education facility, benefit of e-Learning education, 

Cost of e-Learning education, Attitude of e-Learning education and Preference of e-Learning education are shown in Table 

1. 

 
Table 1. Descriptive statistics and normality test result 

 
Sl. No. Questionnaire Variable name N Min Max Kolmogorov– 

Smirnov Test 

(Sig) 

Shapiro–Wilk 

Test (Sig) 

Median 

1. Awareness toward e-Learning        

1. (a) Are you acquainted and involved with e-
Learning 

Awareness1 400 1 5 0.208 (0.000) 0.909 (0.000) 3 

1. (b) e-Learning is web-based learning education 

based on electronically 

Awareness2 400 1 5 0.205 (0.000) 0.887 (0.000) 3 

1. (c) e-learning is a blended approaches that 
integrate online components into traditional 

classes 

Awareness3 400 1 5 0.333 (0.000) 0.758 (0.000) 4 

1. (d) Understanding e-learning examples, 

explanations, assessments, and exercises 

Awareness4 400 1 5 0.217 (0.000) 0.907 (0.000) 3 

2. e-Learning education facility        

2. (a) Internet facility for e-Learning Facility1 400 1 5 0.307 (0.000) 0.845 (0.000) 4 

2. (b) Computer, laptop, modem, etc. facility Facility2 400 1 5 0.298 (0.000) 0.851 (0.000) 4 

2. (c) Access to the requisite technology Facility3 400 1 5 0.277 (0.000) 0.865 (0.000) 4 

2. (d) Instructors' Attitude towards Online 
Education 

Facility4 400 1 5 0.297 (0.000) 0.853 (0.000) 4 

3. Benefits of e-Learning education        

3. (a) e-Learning education is flexible 24/7 Benefit1 400 1 5 0.309 (0.000) 0.769 (0.000) 4 

3. (b) No communication barrier Benefit2 400 1 5 0.313 (0.000) 0.772 (0.000) 4 

3. (c) Convenient for submitting their assignment Benefit3 400 1 5 0.294 (0.000) 0.804 (0.000) 4 

3. (d) Students can easily discuss views with 

classmate 

Benefit4 400 1 5 0.297 (0.000) 0.798 (0.000) 4 

4. Cost of e-Learning education        

4. (a) The Cost of the e-Learning method is higher 
than the traditional method 

Cost1 400 1 5 0.312 (0.000) 0.792 (0.000) 2 

4. (b) E-learning is more costly with electronic 

reading material  

Cost2 400 1 5 0.289 (0.000) 0.808 (0.000) 2 

4. (c) E-learning is a standardized way to deliver 
content 

Cost3 400 1 5 0.290 (0.000) 0.810 (0.000) 2 

4. (d) E-learning is highly costly for sharing 

knowledge and experience 

Cost4 400 1 5 0.310 (0.000) 0.805 (0.000) 2 

5. Attitude to e-learning education        

5. (a) Are you interested in an e-Learning system Attitude1 400 1 5 0.338 (0.000) 0.820 (0.000) 4 

5. (b) e-Learning feel more comfortable Attitude2 400 1 5 0.367 (0.000) 0.777 (0.000) 4 

5. (c) e-Learning feels more effective Attitude3 400 1 5 0.319 (0.000) 0.839 (0.000) 4 

5. (d) E-learning can be used with learning 

exercises that allow learners to apply 

Attitude4 400 1 5 0.341 (0.000) 0.810 (0.000) 4 



Chowdhury et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(2) (2024), 10-18

 

14 

concepts realistically 

6. Preference for e-Learning education        

6. (a) Preferred online for distance learning 

system 

Preference1 400 1 5 0.335 (0.000) 0.821 (0.000) 4 

6. (b) Resources are available in e-Learning Preference2 400 1 5 0.363 (0.000) 0.787 (0.000) 4 

6. (c) Teaching methods are sufficient for e-
Learning 

Preference3 400 1 5 0.369 (0.000) 0.780 (0.000) 4 

6. (d) Online Education is more interactive Preference4 400 1 5 0.358 (0.000) 0.799 (0.000) 4 

 

Each item's minimum and maximum values for the response variables on a five-point Likert scale are 1 and 5, 

respectively. The Kolmogorov– Smirnov test statistic and Shapiro–Wilk test statistic values of each item for awareness 

toward e-learning is 0.205 to 0.333 and 0.758 to 0.909; e-Learning education facility is 0.277 to 0.307 and 0.845 to 0.865, 

the benefit of e-Learning education is 0.294 to 0.313 and 0.769 to 0.804, Cost of e-Learning education is 0.289 to 0.312 and 

0.792 to 0.810, Attitude of e-Learning education is 0.319 to 0.367 and 0.777 to 0.839and Preference of e-Learning is 0.335 

to 0.369 and 0.780 to 0.821 respectively at the significance level 0.000. So, the survey response values are not normally 

distributed, and in this case, median values are considered for mean rank comparison in the non-parametric test. The median 

values of each item for awareness toward e-Learning are 3 and 4; the e-Learning education facility is 4, the benefit of e-

Learning education is 4, the Cost of e-Learning education is 2, the Attitude of e-Learning education is four, and the 

Preference of e-Learning is four respectively. 

 
Factor Analysis 

In the factor analysis, the Kaiser-Meyer-Olkin Measure of Sampling Adequacy value is 0.794 (p = 0.000). So, we can apply 

the factor analysis method to divide the questionnaire response values into different factors (Table 2).  

 
Table 2. Factor analysis, Cronbach's Alpha, and Convergent Validity test result 

 
Rotated Component Matrix  Convergent Square 

 

Component Cronbach’s Validity Root of 

1 2 3 4 5 6 Alpha (AVE) AVE 

Facility2 0.959         

Facility1 0.957      0.985 0.941 0.970 

Facility4 0.941         
Facility3 0.928         

Attitude2  0.871        

Attitude4  0.858     0.911 0.676 0.822 
Attitude1  0.824        

Attitude3  0.822        

Preference3   0.867       

Preference1   0.863    0.902 0.705 0.840 
Preference2   0.845       

Preference4   0.823       

Cost3    0.848      
Cost2    0.846   0.923 0.700 0.837 

Cost1    0.834      

Cost4    0.823      

Benefit4     0.883     
Benefit3     0.867  0.853 0.602 0.776 

Benefit2     0.714     

Benefit1     0.658     

Awareness1      0.937    

Awareness4      0.926 0.844 0.621 0.788 

Awareness2      0.728    
Awareness3      0.689    

Extraction Method: Principal Component Analysis.  

Rotation Method: Varimax with Kaiser Normalization. 

   

 
From the above factor analysis table, the survey response values are classified into six-factor variables such as 

learning education facility (factor leading 0.928 to 0.959), Attitude of e-learning education (factor leading 0.822 to 0.871), 

Preference of e-learning (factor leading 0.823 to 0.867), Cost of e-Learning education (factor leading 0.823 to 0.848), benefit 

of e-Learning education (factor leading 0.658 to 0.883) and awareness toward e-Learning (factor leading 0.689 to 0.937) 

respectively. Here, all factor loadings are more significant than 0.400, which indicates that all measurements for each factor 

are reliable. 

The Cronbach's Alpha value of each factor variable learning education facility is 0.985, the Attitude toward e-

learning education is 0.911, the Preference for e-learning is 0.902, the Cost of e-learning education is 0.923, the benefit of 

e-learning education is 0.853 and awareness toward e-Learning is 0.844, respectively (all the Cronbach's Alpha values are 

>0.7). This indicates that the survey response with the following factors is reliable, valid, and consistent. 

Now, from the above factor analysis, the factor variables are defined by (1) Awareness toward e-learning is 

identified as (a) Are you acquainted and involved with e-Learning (Awareness1), (b) e-Learning is the web-based learning 

education based on the electronic (Awareness2), (c) e-learning is blended approaches that integrate online components into 

traditional classes (Awareness3) and (d) Understanding e-learning examples, explanations, assessments and exercises 



Chowdhury et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(2) (2024), 10-18

 

15 

(Awareness4). (2) e-learning education facility is identified as (a) Internet facility for e-Learning (Facility1), (b) Computer, 

laptop, modem, etc. facility (Facility2), (c) Access to the requisite technology (Facility3), and (d) Instructors attitude towards 

online Education (Facility4). (3) Benefit of e-Learning education is identified as (a) e-Learning education is flexible 24/7 

(Benefity1), (b) No communication barrier (Benefit2), (c) Convenient for submitting their assignment (Benefit3), and (d) 

Students can discuss easily views with classmate (Benefit4). (4) The Cost of e-Learning education is identified as (a) the 

Cost of the e-Learning method is higher than the traditional method (Cost1), (b) e-Learning is more costly with electronic 

reading material (Cost2), (c) e-learning is a standardized way to deliver content (Cost3) and (d) e-Learning is higher costly 

for sharing knowledge and experience (Cost4). (5) Attitude of e-learning education is identified as (a) Are you interested in 

the e-Learning system (Attitude1), (b) e-Learning feel more comfortable (Attitude2), (c) e-Learning feel more effective 

(Attitude3) and (d) e-Learning can be used with learning exercises that allow learners to apply concepts realistically 

(Attitude4). (6) Preference for e-learning education is identified as (a) Preferred online for distance learning system 

(Preference1), (b) Resources are available in e-Learning (Preference2), (c) Teaching methods are sufficient for e-Learning 

(Preference3) and (d) Online Education is more interactive (Preference4). 

Based on the above factor analysis result, a structural equation model of the Attitude of e-learning education from 

awareness toward e-learning, e-learning education facility, benefit of e-learning education, and Cost of e-learning education 

to Preference of e-learning education is developed (Figure 2). 

 
Figure 2. Structure Equation Model 

 
According to the above structural equation model, the standardized regression weights for awareness toward e-

learning are 0.49 to 1.01; e-learning education facility is 0.92 to 1.00; the benefit of e-learning education is 0.46 to 1.01, 

Cost of e-learning education is 0.64 to 1.01, Attitude of e-Learning education is 0.59 to 0.99andpreference of e-Learning 

education is 0.78 to 0.90 respectively (which are nearly between –1 to 1). Each factor loading is exceptionally high and 

statistically significant (p <0.05). The covariance values between e6 and e7is 0.60, e9 and e10 are 0.58, e14 and e15 are 

0.99, and e18 and e20 are 0.93 in the model and are also significant (p < 0.05). 

In the selected model, the model index values observed as χ2/df is 2.321 (which is < 3), comparative fit index (CFI) 

value is 0.975 (which is > 0.9), incremental fit index (IFI) is 0.975 (which is > 0.9), Tucker Lewis index (TLI) is 0.972 

(which is > 0.9), normed fit index (NFI) is 0.957 (which is > 0.9) and Root Mean Square Error of Approximation is 0.058 

(which is < 0.08). Here, the model index values fulfill all of the standard requirements of the survey, and hence, the selected 

model fits well. Now, to validate the model-chosen average variance expected (AVE) to test convergent validity (Table 2). 

 



Chowdhury et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(2) (2024), 10-18

 

16 

The average variance expected (AVE) to test convergent validity (from Table 2) for awareness toward e-Learning 

is 0.621; e-Learning education facility is 0.941; benefit of e-Learning education is 0.602; Cost of e-Learning education is 

0.700; Attitude of e-Learning education is 0.676 and Preference of e-Learning is 0.705 respectively. Here, all the AVE 

values are more significant than 0.5, which indicates that the model has achieved convergent validity. 

 

Table 3. Discriminant validity and path coefficient result 
 

Correlation Estimate 

MSV 

 Path Co-efficient Estimate P 

Awareness <--> Attitude 0.004  Attitude <--- Awareness 0.002 .957 

Facility <--> Attitude 0.319  Attitude <--- Facility 0.233 *** 

Benefit <--> Attitude 0.270  Attitude <--- Benefit 0.457 *** 

Cost <--> Attitude – 0.252  Attitude <--- Cost – 0.099 .028 

Attitude <--> Preference 0.403  Preference <--- Attitude 0.439 *** 

 

To test the discriminant validity, the maximum shared variance (MSV) of Awareness and Attitude is 0.004 (from 

Table 3), which is less than the square root of AVE for Awareness (0.788) and the square root of AVE for Attitude (0.822). 

Again, the maximum shared variance (MSV) of Facility and Attitude is 0.319 (from Table 3), which is less than the square 

root of AVE for Facility (0.970) and the square root of AVE for Attitude (0.822). Also, the maximum shared variance 

(MSV) of Benefit and Attitude is 0.270 (from Table 3), which is less than the square root of AVE for Benefit (0.776) and 

the square root of AVE for Attitude (0.822). Again, the maximum shared variance (MSV) of Cost and Attitude is – 0.252 

(from Table 3), which is less than the square root of AVE for Cost (0.837) and the square root of AVE for Attitude (0.822). 

Moreover, the maximum shared variance (MSV) of Attitude and Preference is 0.403 (from Table 3), which is less than the 

square root of AVE for Attitude (0.837) and the square root of AVE for Preference (0.822). So, the selected model has 

achieved discriminant validity. 

 

Hypothesis Testing 

In the structure equation model, the regression weight (path coefficient) of awareness toward e-learning to the Attitude 

toward e-learning education is 0.002 (p = 0.957). So, the awareness of e-learning has no significant contribution to the 

Attitude toward e-learning education (as the p-value is more critical than 0.05) practice in Bangladesh. So, there is 

insufficient evidence to reject the null hypothesis H1 for awareness of e-learning and the Attitude toward e-learning 

education. As a result, e-learning awareness contributes little to e-learning education's Attitude. 

In the structure equation model, the regression weight (path coefficient) of the e-learning education facility to the 

Attitude of e-learning education is 0.233 (p = 0.000). So, the e-learning education facility significantly contributes to the 

Attitude of e-learning education (as the p-value is less than 0.05) practice in Bangladesh. So, the null hypothesis H2 is 

rejected for e-learning education facilities compared to the Attitude of e-learning education. As a result, e-learning education 

facilities should contribute positively to e-learning education's Attitude. 

In the structure equation model, the regression weight (path coefficient) of e-learning education's benefit to e-

learning education's Attitude is 0.457 (p = 0.000). So, the benefit of e-learning education significantly positively contributes 

to the Attitude of e-learning education (as the p-value is less than 0.05) practice in Bangladesh. So, the null hypothesis H3 

is rejected for the benefit of e-learning education to the Attitude of e-learning education. As a result, the benefit of e-learning 

education contributes positively to the Attitude of e-learning education. 

In the structure equation model, the regression weight (path coefficient) of the Cost of e-learning education to the 

Attitude of e-learning education is – 0.099 (p = 0.028). So, the Cost of e-learning education significantly negatively 

contributes to the Attitude of e-learning education (as the p-value is less than 0.05) practice in Bangladesh. So, the null 

hypothesis H4 is rejected for the Cost of e-learning education to the Attitude of e-learning education. As a result, the Cost 

of e-learning education should contribute negatively to the Attitude of e-learning education. 

In the structure equation model, the regression weight (path coefficient) of the Attitude of e-learning education to 

the Preference for e-learning education is 0.439 (p = 0.000). So, the Attitude toward e-learning education has a significant 

positive contribution to the reference of e-learning education (as the p-value is less than 0.05) practice in Bangladesh. So, 

the null hypothesis H5 is rejected for an attitude of e-learning education to the Preference of e-learning education. As a 

result, the Attitude toward e-learning education should contribute positively to the Preference for e-learning education. 

 

CONCLUSIONS 

The study results show that e-learning education facilities and the benefits of e-learning positively affect the user's Attitude 

toward e-learning education. Besides that, the study reveals that the Cost of e-learning education negatively impacts the 

user's Attitude. However, awareness doesn't have any influence on e-learning. The user attitude toward e-learning also 

contributes to the Preference for e-learning education. Finally, this study reveals that e-learning facilities, benefits, and costs 

significantly impact user attitudes towards e-learning, where facility and benefit have a positive effect, and Cost hurts e-

learning attitudes. Simultaneously, awareness does not substantially affect the form of Attitude. Finally, e-learning attitudes 

significantly formed e-learning preferences. Although there are many advantages, students have different opinions and 

attitudes towards this modern learning process. This study was designed to examine students' attitudes towards e-learning. 

The result suggests that the government may pay attention to infrastructural development like Internet connection, High 

Bandwidth, and Data security to promote positive Attitudes in students, which will help integrate e-learning into the 

educational process. The study could have contributed better if government agencies had been incorporated as respondents, 



Chowdhury et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(2) (2024), 10-18

 

17 

which is one of the significant limitations. Further research will use qualitative techniques applied to small sample users 

using or using an e-learning system. The aim will be to identify problems that appear in the use of IT & C for e-learning.   

 

 
Author Contributions: Conceptualization, M.M.C. and M.S.I.; Methodology, M.S.I.; Software, M.S.I. and K.A.N.; Validation, M.T.R.; Formal Analysis, 

M.S.I. and A.M.S.; Investigation, M.T.R.; Data Curation, M.M.C. and M.T.R.; Writing – A.M.S.; Writing – Review & Editing, M.S.I., M.M.C. and K.A.N.; 
Visualization, M.S.I.; Project Administration, M.M.C.; Funding Acquisition, M.M.C. and K.A.N., A.M.S. and M.T.R. Author has read and agreed to the 

published version of the manuscript.  

Institutional Review Board Statement: Ethical review and approval were waived for this study because the research does not involve vulnerable groups 
or sensitive issues. 

Funding: The authors received no direct funding for this research. 

Acknowledgments: Not Applicable. 
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available upon request from the corresponding author. Due to restrictions, they are not 

publicly available. 
Conflicts of Interest: The authors declare no conflict of interest.    

 

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APPENDICES 

Appendix A: User attitude towards Preference of e-Learning in Bangladesh 

The respondents' information will be kept confidential and used only for research purposes. 

Questionnaire 

 
Name  

Faculty Business Studies  Science and 

Engineering 

 Social Science  Law  

Gender                             Male                     Female  

 Questionnaire Strongly Agree Agree Neutral Disagree Strongly Disagree 

1. Awareness toward e-Learning      



Chowdhury et al., Bangladesh Journal of Multidisciplinary Scientific Research 9(2) (2024), 10-18

 

18 

1a Are you acquainted and involved with e-

Learning? 

     

1b e-Learning is web-based learning education 

based on electronically 

     

1c e-learning is a blended approaches that integrate 
online components into traditional classes 

     

1d Understanding e-learning examples, 

explanations, assessments and exercises 

     

2. e-Learning education facility      

2a Internet facility for e-Learning      

2b Computer, laptop, modem etc. facility      

2c Access to the requisite technology      

2d Instructors Attitude towards online Education      

3. Benefits of E-learning Education      

3a e-Learning education is flexible 24/7      

3b No communication barrier      

3c Convenient for submitting their assignment      

3d Students can easily discuss views with classmates.      

4. Cost of e-Learning education      

4a The Cost of the e-Learning method is higher than 

that of the traditional method. 

     

4b E-learning is more costly with electronic 

reading material. 

     

4c E-learning is a standardized way to deliver 

content. 

     

4d E-learning is highly costly for sharing 

knowledge and experience. 

     

5. Attitude to e-learning education      

5a Are you interested in an e-learning system?      

5b e-Learning feel more comfortable      

5c e-Learning feels more effective      

5d E-learning can be used with exercises that allow 

learners to apply concepts realistically. 

     

6. Preference for e-Learning education      

6a Preferred online for distance learning system      

6b Resources are available in e-Learning.      

6c Teaching methods are sufficient for e-Learning.      

6d Online Education is more interactive.      

 

 
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