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p-ISSN: 2722-399X;  e-ISSN: 2722-1857 
SiLeT, Vol. 2, No. 3, December 2021: 61-72 

©2021 Studies in Learning  
and Teaching 

 

Studies in Learning and Teaching 
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The Impact of Students’ Perceptions of Online Learning Environments 
on Students’ Satisfaction in the Context of COVID-19 Pandemic  

 S G Dastidar1 
1Department of Education, Rabindra Bharati University, India 

Article Info  ABSTRACT 

Article history: 

Received November 13, 2021 
Revised November 28, 2021 
Accepted December 6, 2021 
Available Online December 30, 2021  

In the context of the Covid-19 pandemic, the present study aimed to 
examine students’ perceptions of online learning environments and 
students’ satisfaction based on their academic stream. The study 
also investigated the impact of students’ perceptions of online 
learning environments on students’ satisfaction. A quantitative 
descriptive survey method was applied. This study included 230 
students (130 undergraduate and 100 postgraduate students) from 
colleges and universities of West Bengal. Online Learning 
Environments Survey, an adapted and translated (Bengali) version 
of the Distance Education Learning Environments Survey (DELES) 
by Scott L Walker (2003), was used for collecting data. For data 
analysis, statistical techniques, ANOVA, and regression analysis 
were performed. The results revealed significant mean differences 
among arts, commerce, and science students’ perceptions of online 
learning environments in the dimensions of student interaction and 
collaboration, personal relevance, authentic learning, active 
learning, and student autonomy except in instructor support. 
Furthermore, a significant mean difference in student satisfaction 
was found based on the academic stream. The result revealed that 
overall students’ perceptions of online learning environments had a 
significant impact on student satisfaction, with student interaction 
and collaboration being the most significant predictor of all; 
however, instructor support, active learning, and student autonomy 
were not found to be significant predictors of student satisfaction. 

Keywords: 

Online learning 
Student satisfaction 
Learning environment 
COVID-19 
Pandemic 
Academic STREAM 

 
https://doi.org/10.46627/silet  

INTRODUCTION 
The sudden emergence of the Covid-19 pandemic forced many countries to implement 
lockdown to flatten the curve of the lethal virus (Kamble et al., 2021). This wreaked havoc on 
every sector of life including education sector. The pandemic took a toll on the education 
system with unprecedented challenges. It has shaken the education sector globally (Dhawan, 
2020). All educational institutions were compelled to close their doors for face-to-face teaching 
and learning to prevent the transmission of the virus. It has caused severe disruption of 
education systems, affecting approximately 1.6 billion students across more than 190 countries 
(United Nations, 2020). In this time of turbulence when face-to-face teaching and learning was 
not feasible, emergency remote learning was the only way to ensure continued teaching and 
learning (Hussein et al., 2020; Tang et al., 2021). Educational institutions shifted from the 
traditional model of teaching and learning to the online mode of teaching and learning (Biwer 
et al., 2021; Patricia, 2020). Online learning refers to learning experiences that take place through 
the use of technology (Moore et al., 2011). Online learning has acquired popularity in the field 
of education (Baber, 2020); however, earlier it was considered only as a part of non-formal 

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education (Mishra et al., 2020). The pandemic has increased the pace of online learning. With 
this emergent transition to the online mode of teaching and learning, students experience 
entirely different learning experiences (Adnan & Anwar, 2020) as there is a difference between 
the nature of traditional and online learning environments (Trinidad et al., 2005). Bignoux & 
Sund (2018) stated that the online learning environment varied profoundly from the traditional 
learning environments in terms of students’ motivation, satisfaction, and interaction. 

Over the past few decades, studies showed the significance of the learning environment in 
the process of teaching and learning. Learning environment refers to “the social, physical, 
psychological and pedagogical context in which learning occurs and which affects student 
achievement and attitudes (Fraser, 2012). Physical, social, and organizational environments in 
which teaching and learning processes take place have a pivotal role (Unesco, 2012). The 
learning environment is a significant predictor of student performance (Duruji et al., 2014; 
Shamaki, 2015), academic interest (Ezike, 2018), academic self-efficacy (Daemi et al., 2017). 
Students’ perceptions of the psychological characteristics of their classroom influence students’ 
cognitive and affective learning outcomes (Fraser, 1998). “Good learning environments foster 
quality learning, and bad learning environments do not” (Unesco, 2012, p.9). 

Student satisfaction refers to a “short-term attitude resulting from an evaluation of a 
students’ educational experience” (Elliott & Healy, 2001, p.2). The quality of learning 
experiences is determined by student satisfaction (Kuo et al., 2013).Satisfaction influences 
student motivation (Bolliger & Martindale, 2004); persistence and retention (Sembiring, 2015). 
Learner satisfaction is positively correlated with the quality of learning outcome (Palmer & 
Holt, 2009); students’ success in learning (Muzammil et al., 2020). Students with a high level of 
satisfaction performed better academically than students with a low level of satisfaction 
(Martirosyan, 2014). Student satisfaction is crucial in the adaption of online learning (Zhu, 
2012). 

India like many countries imposed a nationwide lockdown from March 25, 2020 first for 
21days and later extended to 31st May 2020 to limit the spread of deadly coronavirus (Kamble et 
al., 2021). Before that, the Government of West Bengal announced the closure of all educational 
institutions on 14th March 2020 as educational institutions were viewed as the potential hotspots 
for the spread of coronavirus. Colleges and universities quickly shifted to online teaching and 
learning as per the guidelines of the University Grants Commission. In West Bengal, more than 
a year has been passed since students adopted online learning; hence, it is necessary to 
understand how students perceive online learning environments and the factors influencing 
students’ satisfaction in online learning environments to foster student success and academic 
performance. Quality enhancement of online teaching and learning is crucial (Dhawan, 2020). 
Students’ perceptions and satisfaction need to be examined as these determine the quality of 
learning outcomes. This study aimed to examine the impact of students’ perceptions of online 
learning environments on students’ satisfaction in the context of the COVID-19 pandemic.  

Literature Review 
Abbasi et al. (2020) conducted a study on 382 MBBS and BDS students from a medical college to 
examine the perceptions of students towards e-learning during the lockdown. The result 
revealed that students preferred face-to-face teaching and learning to e-teaching during the 
lockdown amid the COVID-19 pandemic. 

Adnan & Anwar (2020) in a study on 126 higher education students from Pakistan reported 
that traditional classroom learning was more effective and motivating than online learning or 
distance education. The study showed that online learning could not produce effective 
outcomes in underdeveloped nations, where the majority of students lack access to the internet 
because of technical and financial constraints. 

Ansar et al. (2020) in a study conducted on 600 students of medical, engineering, and art 
from universities of Pakistan reported that the majority of the students were not satisfied with 

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online learning. Students preferred classroom teaching and did not want to continue with e-
learning. 

Baber (2020) in a study conducted on 100 undergraduate students from South Korea and 
India reported that the factors that had an impact on students’ perception of learning outcome 
and student satisfaction were classroom interaction, student motivation, course structure, 
instructor knowledge, and facilitation. The results showed no significant difference in the 
students’ perceived learning outcome and student satisfaction in the two countries. 

Syauqi et al. (2020) conducted a study on 58 students from Indonesia to examine students' 
perceptions of Mechanical Engineering Education on online learning as a result of the impact of 
the Covid-19 pandemic. The results indicated that teachers’ management with online learning 
did not satisfy students’ expectations. Students felt that online learning could not provide better 
experience or productivity in mastering competencies, but might bring motivation and ease in 
their learning; however, they were unwilling to use it in the future. 

Alqurashi (2017) in a study of 167 undergraduate and graduate students from a university 
of Western Pennsylvania concluded that all four predictor variables of the learning 
environment (online learning self-efficacy, learner-content interaction, learner-instructor 
interaction, and learner-learner interaction) impacted students’ satisfaction and perceived 
learning. The study reported that interaction between learner and content was the most 
significant factor influencing student satisfaction; however, the interaction between learner and 
learner was not a significant predictor of student satisfaction and perceived learning. 

Carver (2014) in a study on 745 high school students from the USA revealed that students 
perceived online learning to be more beneficial than face to face learning in terms of active 
learning and autonomy; though they preferred face to face learning to online teaching and 
learning in terms of student interaction and collaboration and enjoyment. 

Kuo et al. (2013) found that interaction between learner and instructor, the interaction 
between learner and content, and internet self-efficacy were significant determinants of student 
satisfaction; however, the interaction between learner and learner and self-regulated learning 
had no impact on students satisfaction. 

Velayutham et al. (2013) in a study of 352 college students from the United Arab Emirates 
reported that two aspects of learning environments namely teacher support and personal 
relevance were significant predictors of students’ enjoyment of mathematics lessons and 
academic self-efficacy. 

Sahin (2007) in a study of 970 undergraduate students from Turkey reported that four 
dimensions of learning environments that had a significant and positive relation to student 
satisfaction were personal relevance, instructor support, active learning, and authentic learning. 

Trinidad et al. (2005) in a study of 325 students from Australia and Hong Kong reported 
that overall instructors perceived learning environments more preferably than their students. 
The results also indicated a statistically significant association between the e-learning 
environment and student enjoyment. 

Significance of the Study 
A significant body of research has been conducted on the learning environment and student 
satisfaction; however, studies on the online learning environment and students’ satisfaction in 
the online learning environment are very limited in India, particularly in West Bengal. 
Moreover, previous studies were conducted on the learning environment, mostly on the 
traditional classroom learning environment. In the context of the COVID-19 pandemic, the 
present study examined the impact of students’ perceptions of online learning environments on 
student satisfaction in West Bengal, as a new online learning environment has emerged as a 
result of the current pandemic situation, and many researchers opine that this online teaching 
and learning will eventually replace traditional teaching and learning if the situation persists 
(Mishra et al., 2020). Further, very few studies addressed students’ perceptions and satisfaction 

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with the online learning environment based on their academic stream. This study examined the 
students’ perceptions of online learning environments and students’ satisfaction with online 
learning environments based on the academic stream. The findings of the present study will 
contribute to and extend previous literature related to the learning environment and the impact 
of the learning environment on student satisfaction. This study is significant as it provides 
educators and policymakers with insights on how to improve the quality of online learning 
environments, which is dependent on students’ perception and satisfaction. Understanding the 
predictors of student satisfaction in the new online learning environments is crucial as this will 
help the educators, administrators to design content, apply effective techniques, and implement 
teaching strategies so that students may succeed in online learning.  

RESEARCH METHOD 
A quantitative descriptive survey method was used in this study. 

Objectives 
The objectives of this study were:  

1. To find out the significant mean difference among arts, commerce, and science students’ 
perceptions of online learning environments.  

2. To find out the significant mean difference among arts, commerce, and science students’ 
satisfaction. 

3. To study the impact of students’ perceptions of online learning environments on 
students’ satisfaction. 

Hypotheses 
The following hypotheses were formed based on the objectives of the study: 

H01. There was no significant mean difference among arts, commerce, and science students’ 
perceptions of online learning environments.  

H02. There was no significant mean difference among arts, commerce, and science students’ 
satisfaction. 

H03. There was no impact of students’ perceptions of online learning environments on 
students’ satisfaction. 

Population & Sample 
The population of the study consisted of undergraduate and postgraduate students from 
government and government-aided colleges and universities of West Bengal. The sample of the 
study was 230 students including 100 postgraduate and 130 undergraduate students studying 
at the government and government-aided colleges and universities of West Bengal. A random 
sampling technique was used to select the participants for the study. 

Tool and Procedure of Data Collection 
Google forms were sent to the students through WhatsApp and e-mail for conducting an online 
survey. The Online Learning Environments Survey, an adapted and translated version (Bengali) 
of the Distance Education Learning Environment Survey by Scott El Walker, 2003, was used to 
collect data. The present scale consisted of 56 items. Online learning Environments Survey 
includes 6 sub-scales measuring students’ perceptions of the online learning environment, 
namely, instructor support, student interaction and collaboration, personal relevance, authentic 
learning, active learning, and student autonomy. An added effective scale of the Online 
Learning Environments Survey is student satisfaction. 

Statistical Analysis 
For analysing the data ANOVA and regression analysis were conducted using IBM SPSS. 
 

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RESULTS AND DISCUSSION 
H01. There was no significant mean difference among arts, commerce, and science students’ 
perceptions of online learning environments. 
 

Table 1. Results of ANOVA for arts, commerce, and science students’ perceptions of online learning 
environments 

Scales of online 
learning 

environment 
survey 

Stream N Mean SD  df F Sig 

Instructor Support 

Arts 108 41.54 4.335 
Between 
Groups 

2 

1.676 0.189 
Commerce 48 41.38 4.579 

Within 
Groups 

227 

Science 74 40.35 4.455 Total 229 

Student 
interaction and 
collaboration 

Arts 108 28.14 5.610 
Between 
Groups 

2 

4.153 0.017 
Commerce 48 27.56 5.214 

Within 
Groups 

227 

Science 74 25.58 6.855 Total 229 

Personal 
Relevance 

Arts 108 26.80 6.418 
Between 
Groups 

2 

7.137 0.001 
Commerce 48 26.33 5.762 

Within 
Groups 

227 

Science 74 23.42 5.850 Total 229 

Authentic 
Learning 

Arts 108 29.06 5.173 
Between 
Groups 

2 

12.571 0.000 
Commerce 48 27.79 5.194 

Within 
Groups 

227 

Science 74 24.93 5.934 Total 229 

Active Learning 

Arts 108 33.21 5.099 
Between 
Groups 

2 

3.481 0.032 
Commerce 48 32.79 4.993 

Within 
Groups 

227 

Science 74 31.20 5.268 Total 229 

Student 
Autonomy 

Arts 108 41.66 7.158 
Between 
Groups 

2 

5.273 0.006 
Commerce 48 41.46 5.903 

Within 
Groups 

227 

Science 74 38.55 6.383 Total 229 

 
Table 1 showed that obtained p-value was less than the .05 level of significance in all the  

dimensions of online learning environments survey, namely, student interaction and 
collaboration( F= 4.153, p value= 0.017); personal relevance(F= 7.137, p value= 0.001);authentic 
learning(F= 12.571, p value= 0.000) active learning(F= 3.481, p value= 0.032); student 
autonomy(F= 5.273, p value= 0.006) except in one dimension, instructor support (F= 1.676, p 
value= 0.189). It indicated that there was a significant difference among arts, commerce, and 
science students’ perceptions of online learning environments in all sub-scales except in the 
sub-scale of instructor support; hence, the null hypothesis, “there was no significant mean 
difference among arts, commerce and science students’ perceptions of online learning 
environments”, was partially rejected. The mean difference indicated that the students of arts 

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had the best experiences with online learning environments among the three groups of 
students. 

 
H02. There was no significant mean difference among arts, commerce, and science students’ 
satisfaction. 

Table 2. Results of ANOVA for arts, commerce, and science students’ satisfaction 

Descriptives ANOVA 

Variable 
Academic 

Stream 
N Mean SD  df F Sig. 

Student 
Satisfaction 

Arts 108 29.21 8.840 
Between 
Groups 

2 

42.240 0.000 
Commerce 48 26.83 6.231 

Within 
Groups 

227 

Science 74 18.91 6.064 Total 229 
 Total 230 25.40 8.782     

 
From table 2, it was found that obtained p-value was less than the 0.01 level of significance in 
the sub-scale of student satisfaction (F= 42.240 and p-value= 0.000). This indicated that there 
was a significant mean difference among arts, commerce, and science students’ level of 
satisfaction. Hence, the null hypothesis, “there was no significant mean difference among arts, 
commerce and science students’ satisfaction.” was not accepted. The result signified that art 
students scored the highest and science students scored the lowest in their level of satisfaction 
with online learning environments. 
 
H03. There was no impact of students’ perceptions of online learning environments on 
students’ satisfaction. 

Table 3. Model summary for regression analysis 

Model Summary 

Model R R Square 
Adjusted R 

Square 
Std. Error of the 

Estimate 

1 .555a 0.307 0.289 7.406 

 
Table 3 showed that the r-value was .555 that indicated a medium degree of positive correlation 
between students’ perceptions of online learning environments and students’ satisfaction. The 
value of R2 was 0.307, which indicated that students’ perceptions of online learning 
environments could account for 30.7% of the variation in students’ satisfaction. 

Table 4. ANOVA test of students’ perception of online learning environments on student satisfaction 

ANOVAa 

Model  
Sum of 
Squares 

df 
Mean 

Square 
F Sig. 

1 

Regression 5430.494 6 905.082 16.502 .000b 

Residual 12230.706 223 54.846   

Total 17661.200 229    

a. Dependent Variable: Student Satisfaction. 
b. Predictors: (Constant), Student Autonomy, Instructor Support, Student interaction and 

Collaboration, Personal Relevance, Active Learning, Authentic Learning.  
Table 5 reports the overall effect of students’ perceptions of online learning environments 

on student satisfaction. Here, F is 16.502, which is significant at p < .001 (because the value in 

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the column labeled Sig. is less than .001). This result indicated that overall effect of students’ 
perceptions of online learning environment on student satisfaction is significant. 

Table 5. The regression coefficient for students’ perception of online learning environments and student 
satisfaction 

Coefficientsa 

Model 

Unstandardized 
Coefficients 

Standardized 
Coefficients 

t Sig. 

B 
Std. 

Error 
Beta 

1 

(Constant) -2.246 5.307  -0.423 0.672 

Instructor 
Support 

0.038 0.133 0.018 0.287 0.775 

Student 
interaction 

and 
Collaboration 

0.290 0.118 0.200 2.459 0.015 

Personal 
Relevance 

0.250 0.121 0.178 2.073 0.039 

Authentic 
Learning 

0.286 0.142 0.186 2.023 0.044 

Active 
Learning 

0.026 0.153 0.015 0.171 0.864 

Student 
Autonomy 

0.075 0.119 0.058 0.63 0.530 

a. Dependent Variable: Student satisfaction 

 
Table 5 showed that, in the present model, the unstandardized Beta-values (b) were significant 
for student interaction and collaboration, personal relevance, and authentic learning; 
unstandardized beta values for instructor support, active learning, and student autonomy are 
not significant. The unstandardized beta values indicate that if student interaction and 
collaboration increase one unit student satisfaction increases by .290 units. For personal 
relevance, student satisfaction increases by .250 units and for authentic learning, student 
satisfaction increases by .286 units. Student interaction and collaboration were found to have 
the highest predictive value (0.200). 

The findings showed that there was a significant difference among arts, commerce, and 
science students’ perceptions of online learning environments in all sub-scales of the Online 
Learning Environments Survey, namely, student interaction and collaboration, personal 
relevance, authentic learning, active learning, student autonomy, except in one scale, i.e., 
instructor support. Students of arts perceived their online learning environments the best 
during the pandemic situation followed by students of commerce and students of science. There 
might be many reasons causing the differences among students’ perceptions of online learning 
environments based on the academic stream. One reason could be that practical experiments 
and simulations needed for a deep understanding of science concepts were not performed in 
the online classroom (Nsengimana et al., 2021). Also, knowledge construction through 
collaborative learning that was an effective method of science learning was limited in the online 
classroom (Nsengimana et al., 2021). Science students may have problems grasping the concepts 
virtually. However, students did not differ in their perceptions of instructor support, this 

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indicated that students felt that their instructors were supportive, gave prompt feedback, 
encouraged participation, and could be contacted easily. 

The results of the study, further, revealed significant differences among arts, commerce, 
and science students’ satisfaction with online learning environments. The students of science 
were least satisfied and students of arts were most satisfied with online learning environments 
among all groups of students. The reason might be the fact that practical experiments, 
demonstrations, laboratory activities, field trips that are required for science teaching and 
learning were not performed in the online learning classes (Nsengimana et al., 2021). Another 
reason could be that science students preferred face-to-face teaching and learning to online 
teaching and learning (Abbasi et al., 2020; Adnan & Anwar, 2020; Patricia, 2020) and was 
reluctant to use online learning in the future (Syauqi et al., 2020).  

The findings of the study also revealed that overall, students’ perceptions of online learning 
environments had a significant impact on students’ satisfaction; however, the dimensions-
student interaction and collaboration, personal relevance, and authentic learning were 
significant predictors of students’ satisfaction; three sub-scales, instructor support, active 
learning, and student autonomy did not predict students’ satisfaction. The strongest predictor 
of student satisfaction was found to be student interaction and collaboration. This signifies that 
students who have opportunities to interact with their peers, share ideas and information and 
collaborate are more satisfied in online learning environments. This finding is in line with the 
findings of previous studies like Baber (2020), Sher (2009) who reported that student interaction 
fostered student satisfaction. In the present study, personal relevance was found to be another 
significant predictor of student satisfaction. This suggests that in online learning environments, 
students who have opportunities to integrate academic content with personal experiences are 
more satisfied. This finding is consistent with the findings of Sahin (2007) who reported that 
personal relevance was the strongest predictor of student satisfaction. The present study found 
that another significant predictor of satisfaction was authentic learning. This indicates that in 
online learning environments, students become highly satisfied when they can solve real-life 
examples, facts. This finding is in line with the findings of Sahin (2007) and Noreen et al.(2019) 
who reported that authentic learning enhanced students’ satisfaction. The results of the present 
study, however, differed from the previous studies. For example, Sahin (2007) reported that 
instructor support and active learning were predictors of student satisfaction; Bolliger & 
Martindale (2004) found that instructor variables were the most important factor influencing 
student satisfaction in the online learning environment. 

Limitation and Future Research 
The present study has many limitations. First, only students of general courses of colleges and 
universities of West Bengal participated in the study. Further studies can be done on the 
perceptions of online learning environments of students pursuing engineering, vocational, 
medical courses. Secondly, a descriptive survey design was used in this study. Mixed method 
research can be done for in-depth exploration and understanding of students’ perceptions of 
online learning environments and students’ level of satisfaction. Third, the present study was 
done only on 230 students (100 undergraduate and 130 postgraduate students). Future studies 
can be conducted on a large sample of students, also on students of secondary and higher 
secondary level. 

CONCLUSION 
Online learning was not as prevalent as it is in the post-COVID era. The emergence of the 
pandemic has brought a seismic transformation in the field of education as educational 
institutions switched to digital platforms from the four walls of the classroom to ensure 
incessant education. It is overwhelmingly possible that online teaching-learning will continue in 
the coming years as there is uncertainty looming large over the resumption of classes as wave 

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after wave of COVID-19 virus engulfs our country. In this context, the present study aimed to 
investigate students’ perceptions of online learning environments as well as their satisfaction 
based on the academic stream. The study also examined the impact of students’ perceptions of 
online learning environments on students’ satisfaction. The findings showed except in 
instructor support, there existed a significant difference in students’ perceptions of online 
learning environments. Further, a significant difference in their satisfaction with online learning 
environments based on the academic stream was found. The students of arts scored the highest 
while the students of science scored the lowest in their perceptions of online learning 
environments and satisfaction in online learning environments. Further, the results showed that 
overall students’ perceptions of online learning environments had a significant impact on 
students’ satisfaction. The findings of the present study have various suggestions for the 
stakeholders to improve the quality of online learning environments and enhance students’ 
satisfaction. Instructors should provide information; give assessments that are related to 
students’ out-of-class experiences. Activities including collaboration, group assignments should 
be provided. Students should have opportunities to interact among themselves, solve real-
world problems in the online learning environment.  

ACKNOWLEDGEMENTS 
The author thanks all the students participating in this study. 

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Adnan, M & Anwar, K. (2020). Online learning amid the COVID-19 pandemic: Students' 
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Alqurashi, E. (2017). Self-efficacy and the interaction model as predictors of student satisfaction and 
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Author (s): 

* Sunipa Ghosh Dastidar (Corresponding Author) 
Department of Education, 
Rabindra Bharati University, 

10 MB Road, Govt. Qtrs, Block – F, Flat – 3, Belgharia, Kolkata 700083, India 
Email: sunipa25@gmail.com 

 

https://doi.org/10.46627/silet.v2i3.84
https://scie-journal.com/index.php/SiLeT
mailto:sunipa25@gmail.com

