Influence of online attributes and mode of learning in the satisfaction and effectiveness of online learning programs

Abstract

A new paradigm in education is being ushered in by online learning. In the aftermath of the pandemic, online learning has become increasingly available in both academic and professional spheres. Despite difficulties, teachers strive to improve online learning effectiveness. Further emphasis is placed on learning characteristics, readiness, environment, design, and e-learning mode. Whether synchronous or asynchronous learning modes are used will determine the efficacy of the learner. In this study, researchers analyzed the influence of learning qualities on satisfaction and effectiveness, exploring the moderating function of learning mode with 545 online learners’ results. According to this study, the characteristics of online learning significantly impact effectiveness. It also supports the idea that the modality of learning plays a moderating effect in online education. These findings have important practical ramifications for structuring the learning environment and adjusting relevant factors to the satisfaction of students, increasing e-learning efficiency.

Keywords

Asynchronous online mode learning effectiveness online attribute online learning synchronous online mode

Introduction

Online learning became a new norm in the educational system. The student body and the entire educational system have been transformed by new trends in the virtual world. Experiences showed how many students could benefit from more interesting and accessible education, thanks to online learning. According to the World Economic Forum (WEF, 2022), online learning may improve the affordability, accessibility, interactivity, and student-centeredness of higher education with careful design and implementation. Studies portrayed that e-learning is an “effective form of teaching” (Inside Higher Ed’s annual report, 2021). 87% of online undergraduate and graduate students agreed or strongly agreed that online education produces significant results. In 2020, 73% of students were deemed online or partially online, up to 33% in 2017, most likely as a result of COVID-19. Technology has enormous potential to deepen and assist learning outside of the classroom. Technology is an enabler as well as a disruptor. Thus, it urges us to know the tools that are accessible to promote student learning.

According to the new policy, technology is given top priority, with emphasis on regional language e-courses. These will significantly impact planning education, instruction, assessment, and training for teachers and students (NEP, 2020). The Tata Elxsi report (2022) identifies education technology, educational content, entrepreneurship, and e-learning (the 4 E’s) as driving forces in the education sector’s next cycle of development. Over 4450 EdTech firms were established in India between January 2014 and September 2019, reflecting growth in the IT sector and a shift towards education. This prompts developers and instructors to shift focus on improving the effectiveness and satisfaction of e-learning through creating strategic learning environments and attributes.

Online learning is of two types: synchronous and asynchronous. However, some platforms allow for blended, hybrid forms of both (Perveen, 2016). While asynchronous online learning is independent of time and space, synchronous online learning depends on both time and space. Studies show that the learning mode has an impact on how effective and satisfying e-learning is. This study compares the modes of synchronous and asynchronous learning by examining the relationship between learning qualities, learners’ satisfaction, and the effectiveness of online learning.

Though numerous studies exist in the literature on synchronous and asynchronous learning modes, scarce literature examines the moderation effect of learning mode on “effectiveness of learning” in online learning. This study emphasizes a holistic view of e-learning effectiveness by comparing synchronous and asynchronous modes. Additionally, it sheds light on the mediation role of satisfaction in e-learning effectiveness. Subsequent sections include an in-depth analysis of related literature, study materials and methods, findings, and conclusions. Suggestions for further study and potential applications are also provided.

Theory and hypotheses formulation

Influence of learning attributes on satisfaction

Online learning, as defined by Dempsey and Eck (2002), utilizes the internet to provide education to learners distanced by time or distance. Research highlights e-learning’s effectiveness, accessibility, connectivity, convenience (Hill, 2002; Hofmann, 2002; Rourke, 2001; Schrum, 2000). Students prioritize course design and implementation (Singleton et al., 2021), suggesting improved models and procedures could enhance acceptance. Specific attributes like practicality, ease of use, attractiveness, and efficiency significantly impact learner satisfaction (Agyeiwaah et al., 2022). Dziuban et al. (2007) identified engaged learning, agency, and assessment as key components of satisfaction. Students prefer active learning environments over passive ones, given their highly interactive world (Dziuban et al., 2013).

The use of the audio feature by students (both for posting comments and for listening) was a statistically significant predictor of their involvement with their classmates (Mejia, 2020). According to Bao (2020): (1) relevance between online instructional design and student learning; (2) support offered to students; (3) effective delivery of online instructional information; (4) high-quality participation; and (5) contingency plan to deal with unforeseen incidents of online education platforms are the five high-impact principles for online education. While Dziuban et al. (2007) suggested six key components of the value of the course as effective learning environment, instructor commitment, well-defined rules of engagement, reduced ambiguity, an engaging environment, and reduced ambivalence. Ke and Kwak (2013) proposed five components: learner relevance, active learning, learner autonomy, authentic learning, and technical competence. Similarly, Ho et al. (2021) suggested interactive learning, nature of the program, and learning culture whereas Kuo et al. (2013), found technology, instructor, and learner-content engagement are reliable indicators of students’ positive evaluations. Battalio (2007) urged for a successful learner-instructor relationship for a positive learning experience. How students’ expectations affect the instructor’s design of efficient technology tools in online courses is the key to comprehending the satisfaction (Li et al., 2021). The “Student Satisfaction Index Model,” found that higher levels of student satisfaction occur when students’ expectations are met (Bates and Kaye, 2014; Zhang et al., 2008).

Thus, we hypothesize,

Online learning attributes influence the satisfaction of online learning

Satisfaction and effectiveness of learning

According to Darius et al. (2021), effective online learning is supported by online quizzes with multiple-choice questions, availability of student version software, animations, video lectures, a comfortable home environment, faculty interactions during class sessions, and the caliber of online materials. There are three main components for the effectiveness of online learning: (1) students’ preference for the method of delivery; (2) confidence of students in using electronic communication; and (3) capacity for independent learning (Warner et al., 1998). The effectiveness of learning depends upon the interaction, consistency in course design (Swan 2001), the instructor-student interaction (Hay et al., 2004; Picciano, 2002), rate of interaction in the online setting (Arbaugh, 2000; Hay et al., 2004). The flexibility of online learning (Chizmar and Walbert, 1999), and the technological skills necessary (Wagner and Schramm, 2002). Also, well-structured course material, knowledgeable teachers, cutting-edge technology, feedback, and clear directions are all necessary for effective online courses (Gilbert, 2015; Gopal et al., 2021; Sun and Chen, 2016). Also, a professional with exceptional teaching abilities and knowledge of how to suit students’ learning needs is referred to as an “instructor of high quality” (Luekens et al., 2004). A resilient feedback system has been demonstrated to aid in building a strong bond between teachers and students will result in higher learning outcomes (Chang, 2011; Simsek et al., 2017).

In this context, we hypothesize,

Satisfaction with online learning is a positive function of the effectiveness of online learning

Online learning attributes influence the effectiveness of online learning

Influence of mode of learning on effectiveness

Synchronous, asynchronous, and hybrid learning environments are the three categories of online learning (Perveen, 2016). An instructor’s lecture with the option of a QandA session requires that both students and teachers be present at the same time, and synchronous learning settings enable in-person contact that can be collaborative in nature (Salmon, 2013). These are conducted in real-time, which are live online courses where instructors will conduct the session at a pre-specified date and time, using an online meeting device. Asynchronous settings, on the other hand, are not time-restricted and allow students to complete e-activities at their own leisure; in an asynchronous class format, the faculty member posts recorded lectures so students can view them later. It allows students the flexibility of learning time, procedure, and content because it makes learning available whenever it is needed (Hammond et al., 1995). In a hybrid online environment, synchronous meetings and asynchronous e-activities are integrated. The fact that everyone is online at the same moment during synchronous meetings may promote a sense of presence, immediacy, and community online. As opposed to synchronous learning, where students can ask questions at any moment and the teacher must answer immediately, asynchronous learning allows for independent learning. (Cereijo et al., 2001).

Mapping participants’ satisfaction and perceived learning in asynchronous mode, Swan (2001) identifies design clarity, communication with professors, and engaged class participation as crucial elements in determining how satisfied students are with their education. According to Mc Brien et al. (2009) and Somenarain et al. (2010), it’s crucial to understand how children view both types of learning activity. Asynchronous environments promote self-directed, independent learning that is student-centred (Murphy et al., 2011). Therefore, asynchronous online instruction can supplement students’ prior knowledge with fresh ideas (Wang et al., 2021). There should be less reliance on memory and notes and more opportunities for peer dialogues in order to promote critical thinking and deep learning (Huang and Hsiao, 2012). The distanced approach lowers teacher fear by reducing timidity. While there is less stress than in a real-world scenario, the affective filter is still low, allowing pupils to react more quickly and creatively. The risk of becoming irritated by technological problems, such as slow speed and lack of connectivity, is limited because there is plenty of time to test out e-activities. A study on the effects of synchronous and asynchronous computer-mediated communication (CMC) by Ali and Qatawneh (2013) and Lin et al. (2012) found that students who used the asynchronous CMC mode produced noticeably more discourse functions related to question types and strategies than students in the other treatment condition (synchronous group). In contrast to the asynchronous CMC mode, which encouraged students to ask a series of questions that required lengthy responses and sought out more information through examples, clarification, and extension, the synchronous CMC mode supports question types and strategies that are based on short answers that are precise and unambiguous. According to a study conducted in 2009 by Skylar, synchronous and asynchronous courses are both equally effective in terms of delivery. But 75% of students said they favored synchronous courses over asynchronous ones. According to Hrastinski (2008), asynchronous online learning helps students manage their studies with work and family responsibilities while also improving their contributions, which are seen as being more thoughtful than those made during synchronous learning. According to Yamagata-research, Lynch’s found that synchronous online whole-class sessions and carefully designed small group meetings “may help students develop a better sense of connection to their classmates and teacher and stay engaged with course activities” [10, p. 189]. As students interact with classmates in a synchronous setting and receive feedback from professors in real time, their engagement and sense of community increase (Yamagata-Lynch et al., 2013; Francescucci and Rohani, 2018).

Considering this, we propose the hypothesis,

Mode of learning has a moderating effect between online learning attributes and satisfaction with online learning

Methodology

To get clarity on the concept, “online class” has been defined as, “a learning process facilitated through internet platforms and various multimedia modes which support students’ learning with the flexibility of space and time in both synchronous and asynchronous formats that can connect participants and teachers of diverse geographical backgrounds”. Methodology, data collection, and analysis are conducted based on this premise. To predict the effects of various factors on students’ evaluations of the effectiveness of online learning, a model (Figure 1) was developed. In order to analyze the model, 545 online students who use different online platforms were surveyed online. This study was performed during the period from Dec 2022 and February 2023. A structured online survey form was used to collect data. Respondents for the survey have been taken from many institutions and also from individuals who are undergoing online learning. Those respondents who are not using online learning modes were excluded from the data. The sample consists of 54% females and respondents with ages ranging from 15 to 50. According to research, 73% of people use smartphones, 21% use laptops, 5% use tablets, and 1% use other gadgets to learn online. A synchronous online system was used by 368 of the total 545 respondents, while an asynchronous learning mode was used by the other 177 respondents. It is observed that almost 45% of the respondents are comfortable with online learning systems however, many respondents opinioned that they are distracted with various activities at home. The major disturbances at home for online learning are from a colleague; however, the majority of the respondents (59%) didn’t face any disturbances at home. The majority of the respondents prefer to have Synchronous learning mode with both the camera and mike switched off. Furthermore, about 32% of online learners prefer the asynchronous mode of learning. The majority of the respondents prefer to have Synchronous learning mode with both the camera and mike switched off. Also, about 32% of online learners prefer the asynchronous mode of learning. With the use of the AMOS programme, regression weights for each variable were calculated using maximum likelihood estimates. Based on the variance in critical ratio values and regression values obtained for the two groups, the moderating effect was evaluated. The Z scores obtained were taken for analysing the degree of moderation effect and also tested the overall validity of the hypothetical model. Excel statistics were used to compare the regression weights and p values, and the regression weights and Z scores were compared.

Figure 1.

SEM model tested.

Data analysis and results

The issues of missing values and outliers in the data collected from respondents were first looked at. According to reports, the acquired data did not include any anomalies or problems with missing values. The study items’ skewness and kurtosis were then verified in order to examine the univariate normality. The results indicate that there were items with standardized values outside of the range of 1.96, and that skewness of all the items was in the range of zero and kurtosis was in the range of three, assuming that responses to all the items used in the data analysis are reasonably free from skewness and kurtosis (Brown, 2006; Chou and Bentler, 1995; Hair et al., 1998; Kline, 2005; Norusis, 1990). This demonstrates how multivariate theory is implicitly supported.

Measurement model

Once the relevant structural equation modeling (SEM) assumptions were established, confirmatory factor analysis (CFA) was utilized in the study to assess the measurement model and validate the constructs and measurement attributes (Figure 1). We have used proposed goodness of fit indices, such as CFI, IFI, RMSEA, and SRMR, to evaluate the measurement model’s goodness of fit in addition to the conventional Chi-square values. The results of a measurement model on the learning attribute CFI = 0.99; IFI = 0.99; RMR = 0.015, RMSEA = 0.066), and for learning effectiveness, CFI = 0.98; IFI = 0.98; RMR = 0.026, RMSEA = 0.072) supported the idea of fit. Additionally, the study investigated the dimensions in line with Netemeyer et al. (2003) results for validity and reliability (2003). Similarly, all of the values of Composite Reliability (CR) exceeded the advised cutoff point of 0.80. Additionally, all of the Average Variance Extracted (AVE) values exceeded the specified cutoff point of 0.50. These results confirmed the measurement constructs’ convergence validity and reliability. Additionally, we evaluated the discriminant validity of the research constructs by examining the square of each pair of correlations with AVE values. The results demonstrated the discriminant validity by showing that the AVE values were consistently greater than the correlation square between the pairs.

Hypotheses testing

The study’s main objective was to employ Structural Equation Modeling to assess the given set of hypotheses after the measurement model had been verified. According to the structural model results (Table 2), the model fits the data quite well (χ2 = 335.75, df = 120, CFI = 0.97, IFI = 0.97, RMR = 0.053, RMSEA = 0.042). All the hypotheses are tested valid at a 5% level of significance. The model fit indices are all within the acceptable range (Table 1). The effect of moderation was tested with two sub-models wide synchronous and asynchronous learning modes. The comparison of regression estimates, critical ratios and p values are shown below in Table 3 and Table 4. It was found that the p values corresponding to each variable relationship are significant except for the direct relationship between online attributes and the effectiveness of online learning in the asynchronous sub-model. Since the Z-score values for checking the significance of group-wise differences between regression estimates of two paths are not within the range between −1.96 and +1.96 (i.e. Satisfaction causes online effectiveness in the asynchronous learning mode and the effect of online attributes on online effectiveness in synchronous learning mode), it is deduced that there is an effect of moderation in the relationship between satisfaction and online effectiveness, particularly in the asynchronous learning mode and a significant effect of moderation in the direct path between online attributes and effectiveness in the synchronous learning mode. Online learners undergoing degree courses are more tempted to use synchronous learning mode with both camera and mike switched off whereas postgraduates have tendencies to follow synchronous (live class with only mike switch off) (Table 5).

Table 1.

Hypotheses.

Sl. No.	Hypotheses	Regression estimate	p value	Result
H1	Online learning attributes influence the satisfaction of online learning	0.966	***	Accepted
H2	Satisfaction of online learning is a positive function of the effectiveness of online learning	0.739	***	Accepted
H3	Online learning attributes influence the effectiveness of online learning	0.158	.002	Accepted
H4	Mode of learning has a moderating effect between online learning satisfaction and online learning effectiveness	0.674 (Synchronous)	***	Accepted
H4		0.901 (Asynchronous)	***	Accepted

Table 2.

Model fit summary.

CMIN
Model	NPAR	CMIN	DF	p	CMIN/DF
Default model	78	335.753	120	.000	2.798
Saturated model	198	.000	0
Independence model	33	9508.959	165	.000	57.630

RMR, GFI
Model	RMR	GFI	AGFI	PGFI
Default model	.053	.945	.909	.573
Saturated model	.000	1.000
Independence model	.927	.199	.039	.166

Baseline Comparisons.
Model	NFI	RFI	IFI	TLI	CFI
Model	Delta1	rho1	Delta2	rho2	CFI
Default model	.965	.951	.977	.968	.977
Saturated model	1.000		1.000		1.000
Independence model	.000	.000	.000	.000	.000

RMSEA
Model	RMSEA	LO 90	HI 90	PCLOSE
p CLOSE	.042	.037	.047	.993
Independence model	.236	.232	.240	.000

HOELTER
Model	HOELTER	HOELTER
Model	.05	.01
Default model	445	483
Independence model	23	25

Table 3.

Regression Weights: (Synchronous - Default model).

			Estimate	S.E.	C.R.	p
Satisfaction	<---	Attributes	1.010	.072	14.078	***
Online_effectiveness	<---	Satisfaction	.674	.054	12.564	***
Online_effectiveness	<---	Attributes	.224	.063	3.548	***

Table 4.

Regression Weights: (Asynchronous - Default model).

			Estimate	S.E.	C.R.	p
Satisfaction	<---	Attributes	.872	.075	11.576	***
Online_effectiveness	<---	Satisfaction	.901	.092	9.757	***
Online_effectiveness	<---	Attributes	.008	.086	.089	.929

Table 5.

Test of Moderation effect on the effectiveness of online learning caused by learning mode type.

			Synchronous		Asynchronous		z-score
			Estimate	p	Estimate	p	z-score
Satisfaction	<---	Attributes	1.010	0.000	0.872	0.000	−1.328
online_effectiveness	<---	Satisfaction	0.674	0.000	0.901	0.000	2.13**
online_effectiveness	<---	Attributes	0.224	0.000	0.008	0.929	−2.022**

Notes: *** p-value <.01; ** p-value <.05; * p-value <.10.

Discussion and implications

Understanding the impact of learning attributes on student satisfaction and subsequent learning effectiveness in online learning was the goal of the study. The study also looked at how synchronous and asynchronous learning modalities affected learning outcomes. The results suggested that learning attributes had a major impact on students’ satisfaction. The dimension satisfaction of students over learning adds to the learning effectiveness and also works as a mediator between learning qualities and learning effectiveness in e-learning programs. One of the implications of COVID-19 is a rise in the prevalence of online education. In higher education and professional learning environments, in particular, it takes on a new order. The development of ed-tech, improvements in e-learning technology, the rapid pace of the learning ecosystem, the increased importance of technology in education, etc., gave rise to an emphasis on the characteristics of online learning, learner satisfaction, and participant satisfaction metrics. The results of the study shed light on learning qualities, which are a key factor in determining students’ pleasure and the efficiency of online learning.

Certain online characteristics, such as usability, appeal, usefulness, and efficiency, which have a significant impact on learning satisfaction, are used to support the main conclusions (Chen, et al., 2020; Dhawan, 2020; Wang et al., 2021). Online courses benefit from being flexible and student-centred, much like conversations in the classroom that give participants time to think before responding to questions. Because it provides greater flexibility and ongoing learning after lessons are ended. According to Roca et al. (2006), perceived utility, information quality, confirmation, service quality, system quality, perceived ease of use, and cognitive absorption all affect user satisfaction. To increase the efficacy and quality of e-learning, it is crucial to emphasize the quality of educational content, educational technologies, and educational outcomes (Voitovich, 2014). Microblogging as an asynchronous learning technique also enhances learning. Regardless of whether learning is carried out synchronously or asynchronously, the pedagogical model of online learning emphasizes the learner’s accountability and capacity for accountability. Participants in an online course must engage with one another in order to be effective. So, educators must actively encourage student participation. Performance is evaluated using grades, quizzes, independent/standardized exams, and student evaluation of learning, which is the students’ appraisal of how much they learned during the course. Instructors must choose aesthetically appealing resources.

Students’ assessments of factors like flexibility, utility, structure, perceived use of online platforms, and instructor quality in the online learning environment will undoubtedly affect how happy they are with their learning experience. Online users’ emotional evaluations of their experiences are referred to as their level of satisfaction, as is the inherently satisfying result that results from actions that live up to their expectations. These feelings are frequently brought on by many characteristics, which when recognized can help with measures to raise student achievement and the standard of online instruction. Additionally, poor internet connections, a lack of technical know-how, subpar hardware and software, and a lack of learner orientation can all compromise online learning. Making a concerted effort to create an engaging and stimulating online learning environment is necessary to increase students’ happiness with online learning. Providers of online learning platforms need to go beyond the standard, straightforward activities of the online classroom and incorporate features that can improve specialized training for students studying hospitality, whose courses call for some practical abilities.

Scope for further research

Despite myriad studies available on the effectiveness of online learning, there are areas where more studies are possible. The effectiveness of online learning depends on the decisive attributes administered in the process. There are multiple factors including physical and psychological dimensions which are contributing to the effectiveness of online learning. Furthermore, implications of various learning attributes among different categories of students are also to be explored. It is also evident that apart from learning attributes, there are diverse variables that play mediating and moderating roles in online learning effectiveness. The role of other cross-sectional and demographic variables also can be explored.

Online learning has become ingrained in the learners’ community, especially post-COVID-19. Professionals increasingly recognize the value of upskilling through online platforms. This study delved into the role of learning attributes in online learning effectiveness and satisfaction, and explored the moderating influence of the learning mode. It was evident that learning attributes and modes significantly impact online learning effectiveness. Technical features, instructor involvement, student engagement, and environmental settings are vital for effective online learning. With the rise of EdTech and e-learning platforms, studies in this area contribute significantly to improving online learning quality and advancing EdTech ventures.

Conclusion

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Paul V Mathew

Data availability statement

All the data used for the study are available for verification – up on request.*

Author Biographies

Paul V Mathew, PhD, is an esteemed educator and project management expert with over 15 years of experience in the development sector. Specializing in entrepreneurship and skill development, Dr. Mathew has made significant contributions to the field through his expertise and dedication.

Dr. Joemon Pappachan, PhD, serves as a Professor at CIASL Aviation Academy in Kochi. With a distinguished career spanning over 30 years, Dr. Pappachan is a respected researcher in education and marketing management. His extensive experience and scholarly contributions have greatly enriched the fields in which he works.

References

Agyeiwaah

Baiden

Gamor

, et al. (2022) Determining the attributes that influence students’ online learning satisfaction during COVID-19 pandemic. Journal of Hospitality, Leisure, Sports and Tourism Education 30: 100364.

Ali

FAS

Qatawneh

(2013) Effects of synchronous and asynchronous computer-mediated communication (CMC) oral conversations on English language learners' discourse functions, Computers and Education, 62: 181–190, ISSN 0360-1315. DOI: 10.1016/j.compedu.2012.10.013. https://www-sciencedirect-com-443.web.bisu.edu.cn/science/article/pii/S0360131512002412

Arbaugh

(2000) How classroom environment and student engagement affect learning in Internet-based MBA courses. Business Communication Quarterly 63(4): 9–26. DOI: 10.1177/108056990006300402.

Bao

(2020) COVID‐19 and online teaching in higher education: a case study of Peking University. Human behavior and emerging technologies 2(2): 113–115.

Bates

Kaye

(2014) “I’d be expecting caviar in lectures”: the impact of the new fee regime on undergraduate students’ expectations of higher education. Higher Education 67(5): 655–673.

Battalio

(2007) Interaction online: a reevaluation. Quarterly Review of Distance Education 8(4): 339–352.

Brown

(2006) Conceptions of curriculum: a framework for understanding New Zealand’s curriculum framework and teachers’ opinions. Curriculum Matters 2(164): 164–181.

Cereijo

MVP

Young

Wilhelm

(2001) Factors facilitating learner participation in asynchronous Web-based courses. Journal of Computing in Teacher Education 18(1): 32–39.

Chang

(2011) Pre-service Teachers' views: how did E-feedback through assessment facilitate their learning? The Journal of Scholarship of Teaching and Learning 11(2): 16–33.

10.

Chen

Peng

Yin

, et al. (2020) Analysis of user satisfaction with online education platforms in China during the COVID-19 pandemic. Healthcare 8(3): 200–226.

11.

Chizmar

Walbert

(1999) Web-based learning environments guided by principles of good teaching practice. The Journal of Economic Education 30(3): 248–259.

12.

Chou

Bentler

(1995) Estimates and Tests in Structural Equation Modeling. Sage Publications, Inc.

13.

Darius

PSH

Gundabattini

Solomon

(2021) A survey on the effectiveness of online teaching–learning methods for university and college students. Journal of The Institution of Engineers (India): Series B 102: 1325–1334. DOI: 10.1007/s40031-021-00581-x.

14.

Dempsey

Eck

RNV

(2002) Instructional design on-line: evolving expectations. In: Reiser

Dempsey

(eds) Trends and Issues in Instructional Design and Technology. New Jersey: Pearson Education, 281–294.

15.

Dhawan

(2020) Online learning: a Panacea in the time of COVID-19 crisis. Journal of Educational Technology Systems 49(1): 5–22. DOI: 10.1177/0047239520934018.

16.

Dziuban

Moskal

Brophy

, et al. (2007) Student satisfaction with asynchronous learning. Journal of Asynchronous Learning Networks 11(1): 87–95.

17.

Dziuban

Moskal

Kramer

, et al. (2013) Student satisfaction with online learning in the presence of ambivalence: looking for the will-o’-the-wisp. Internet and Higher Education 17: 1–8. DOI: 10.1016/j.iheduc.2012.08.001.

18.

Francescucci

Rohani

(2018) Exclusively synchronous online (VIRI) learning: the impact on student performance and engagement outcomes. Journal of Marketing Education 41(1): 60–69. DOI: 10.1177/02734753188188.

19.

Gilbert

(2015) Online Learning Revealing the Benefits and Challenges. St. John Fisher University, New York: Fisher Digital Publications.

20.

Gopal

Singh

Aggarwal

(2021) Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19. Education and Information Technologies 26(6): 6923–6947.

21.

Hair

Anderson

Tatham

, et al. (1998) Multivariate Data Analysis. New Jersey: Prentice Hall.

22.

Hammond

McKendree

Reader

, et al. (1995) The PsyCLE project: educational multimedia for conceptual understanding. In: ACM Multimedia, San Fransisco, California, USA, Vol. 95, pp. 447–456. ACM.

23.

Hay

Hodgkinson

Peltier

, et al. (2004) Interaction and virtual learning. Strategic Change 13(4): 193–204.

24.

Hill

(2002) Overcoming obstacles and creating connections: community building in web-based learning environments. Journal of Computing in Higher Education 14(1): 67–86.

25.

IMK

Cheong

Weldon

(2021) Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques. PLoS One 16(4): e0249423.

26.

Hofmann

(2002) Internet-based distance learning in higher education. Tech Directions 62(1): 28–32.

27.

Hrastinski

(2008) Asynchronous and Synchronous E-Learning. Colorado: EDUCAUSE Review.

28.

Huang

Hsiao

(2012) Synchronous and asynchronous communication in an online environment: faculty experiences and perceptions. Quarterly Review of Distance Education 13(1): 15–30.

29.

Kwak

(2013) Constructs of student-centered online learning on learning satisfaction of a diverse online student body: a structural equation modeling approach. Journal of Educational Computing Research 48(1): 97–122.

30.

Kline

(2005) Principles and Practice of Structural Equation Modeling. New York: The Gilford press.

31.

Kuo

Walker

Belland

, et al. (2013) A predictive study of student satisfaction in online education programs. International Review of Research in Open and Distance Learning 14(1): 16–39.

32.

Islam

AYMA

(2021) Factors engaging college students in online learning: an investigation of learning stickiness. Business Management 14(12): 215824402110591. DOI: 10.1177/21582440211059181.

33.

Lin

Hong

Lawrenz

(2012) Promoting and scaffolding argumentation through reflective asynchronous discussions. Computers & Education 59(2): 378–384.

34.

Luekens

Lyter

Fox

(2004) Teacher Attrition and Mobility: Results From the Teacher Follow-Up Survey, 2000–01 (NCES 2004–301). Washington, DC: National Center for Education Statistics, U.S. Department of Education. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2004301

35.

Mc Brien

Cheng

Jones

(2009) Virtual spaces: employing a synchronous online classroom to facilitate student engagement in online learning. International Review of Research in Open and Distance Learning 10(3), Retrieved from https://www.irrodl.org/index.php/irrodl/article/view/605/1358

36.

Mejia

(2020) Using VoiceThread as a discussion platform to enhance student engagement in a hospitality management online course. Journal of Hospitality, Leisure, Sports and Tourism Education 26: 100236.

37.

Murphy

Rodríguez-Manzanares

Barbour

(2011) Asynchronous and synchronous online teaching: perspectives of Canadian high school distance education teachers. British Journal of Educational Technology 42(4): 583–591.

38.

NEP (2020) National Education Policy. Ministry of Education, Government of India.

39.

Netemeyer

Bearden

Sharma

(2003) Scaling Procedures: Issues and Applications. Thousand Oaks, CA: Sage publications.

40.

Norusis

(1990) Statistical Package for Social Sciences. Statistical Data Analysis.

41.

Perveen

(2016) Synchronous and asynchronous e-language learning: a case study of virtual university of Pakistan. Open Praxis 8(1): 21–39.

42.

Picciano

(2002) Beyond student perceptions: issues of interaction, presence, and performance in an online course. Journal of Asynchronous Learning Networks 6(1): 21–40.

43.

Roca

Chiu

C-M

Martínez

(2006) Understanding e-learning continuance intention: an extension of the Technology Acceptance Model. International Journal of Human-Computer Studies 64(8): 683–696.

44.

Rourke

(2001) Online learning: fad or fate? Principal Leadership 1(9): 8–14.

45.

Salmon

(2013) The key to active online learning. Routledge 2. DOI: 10.4324/9780203074640.

46.

Schrum

(2000) Guarding the promise of online learning. The Education Digest 66(4): 43–47.

47.

Simsek

Turan

andSimsek

(2017) Social studies teachers‟ and teacher candidates” perceptions on prompt feedback and communicate high expectations. PEOPLE: International Journal of Social Sciences, 3(1), 332, 345.

48.

Singleton

Hill

andKoh

, et al. (2021) Online Learning: Perceptions of Useful and Challenging Characteristics. Retrieved on 5th July 2021, from https://www.researchgate.net/publication/253691013

49.

Somenarain

Akkaraju

Gharbaran

(2010) Student perceptions and learning outcomes in asynchronous and synchronous online learning environments in a biology course. MERLOT Journal of Online Learning and Teaching 6(2): 353–356, Retrieved from https://jolt.merlot.org/vol6no2/somenarain_0610.pdf

50.

Sun

Chen

(2016) Online education and its effective practice: a research review. Journal of Information Technology Education: Research 15: 157–190.

51.

Swan

(2001) Virtual interaction: design factors affecting student satisfaction and perceived learning in asynchronous online courses. Distance Education 22(2): 306–331.

52.

Tata Elxsi - Impact of the Pandemic on the Edtech Sector: An India Perspective (2022) Tata Elxsi - Impact of the Pandemic on the Edtech Sector: An India Perspective. Retrieved October 18, 2022, from https://www.tataelxsi.com/news-and-events/impact-of-the-pandemic-on-the-edtech-sector-an-india-perspective

53.

Voitovich

(2014) Criteria of effectiveness of electronic education and of quality of electronic educational programs in high schools. Almanac of TSPU (Tatarstan State Pedagogical University) 4(145): 152–156.

54.

Wagner

Schramm

(2002) An evaluation of student satisfaction with distance learning courses. Annual conference on distance learning. University of Wisconsin, Whitewater, WI (August).

55.

Wang

Lin

(2021) Continuance intention of university students and online learning during the COVID-19 pandemic: a modified expectation confirmation model perspective. Sustainability 13(8): 4586.

56.

Warner

Christie

Choy

(1998) Readiness of VET Clients for Flexible Delivery Including On-Line Learning. Brisbane: Australian National Training Authority.

57.

WEF , 2022. How Online Learning Could Revolutionize Higher Education. (nd). World Economic Forum. Retrieved October 13, 2022, from. https://www.weforum.org/agenda/2022/06/online-learning-higher-education-covid-19/

58.

Yamagata-Lynch

Click

Smaldino

(2013) Activity systems as a framework for scaffolding participant reflections about distance education in an online instructional technology course. Reflective Practice 14(4): 536–555. DOI: 10.1080/14623943.2013.809336.

59.

Zhang

Han

Gao

(2008) Empirical study on the student satisfaction index in higher education. International Journal of Business and Management 3(9): 46–51.