Key Determinants of Student Satisfaction in Online Learning During COVID-19: Evidence From Vietnamese Students

Thanh, Le Phuoc; Trang, Tran Ngoc Quynh; Minh, Nguyen Nhat; Van Hai, Hoang

doi:https://doi.org/10.1155/2024/5560967

Human Behavior and Emerging Technologies

On this page

Abstract Introduction Results Conclusions Conflicts of Interest Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2024 | Article ID 5560967 | https://doi.org/10.1155/2024/5560967

Key Determinants of Student Satisfaction in Online Learning During COVID-19: Evidence From Vietnamese Students

Le Phuoc Thanh,¹Tran Ngoc Quynh Trang,¹Nguyen Nhat Minh,²and Hoang Van Hai¹

Academic Editor: Pinaki Chakraborty

Received08 Aug 2023

Revised16 Mar 2024

Accepted19 Mar 2024

Published10 May 2024

Abstract

The adoption of online learning modalities has increasingly become prevalent, particularly with the advent of COVID-19, aiming to ensure student access to learning materials. This significant shift towards offering online educational formats compels educational institutions to alter their approach and develop curricula to guarantee an optimal student experience and satisfaction within the online environment. The aim of this research is to comprehensively examine the key factors that significantly impact the satisfaction of undergraduate students with online learning in Vietnamese universities. The quantitative research methodology was implemented through the collection of surveys from a total of 437 Vietnamese students. Utilizing the PLS-SEM statistical approach, the findings reveal that technology, communication, course, outcome, and motivation for learning have significant positive influences on students’ satisfaction with online education during the COVID-19 pandemic, while the effect of instructors’ attitude and the sudden change from traditional to online classes have been found with as nonsignificant. Valuable implications and practical recommendations are suggested for educational organizations and institutions in Vietnam to enhance specific activities that promote students’ satisfaction with online learning and improve teaching methods provided by instructors.

Keywords: COVID-19 pandemic; global education system; higher education; online learning; satisfaction; user experience questionnaires; Vietnamese

1. Introduction

The COVID-19 pandemic profoundly impacted the global education system. Escalating cases led to school closures and the urgent need to shift to online education [1, 2]. As a result, higher education worldwide had to adapt to unconventional online teaching and learning environments to address the pandemic emergency. However, there is limited educational research on specific online factors that significantly affect learning enjoyment, such as usability, appeal, practicality, and proficiency.

The accessibility of learning materials plays a pivotal role in ensuring the quality of students’ learning experiences and performance [3, 4]. Directly related to student engagement, ease of access to learning resources allows students to be more proactive in seeking out materials, thereby enhancing their ability to learn independently, boosting confidence, and consequently reducing stress or burnout in the learning process by facilitating straightforward access to reference materials and support [5]. Additionally, it is instrumental in promoting lifelong learning and supporting the development of a more diverse and personalized learning experience, wherein students can find resources aligned with their preferred learning methodologies.

Educational institutions used to predominantly rely on traditional approaches to access learning materials and organize learning formats physically, including in-person classes and other tangible resources [6]. However, this mode encountered significant challenges during the COVID-19 pandemic when physical attendance was severely limited by social distancing policies over an extended period globally [4]. This situation led to substantial disruptions in the learning experience and students’ access to learning materials as physical classes could not be organized, and students faced difficulties in reaching support from lecturers or accessing other educational support resources [7]. COVID-19 has precipitated profound changes and directly impacted how learning materials are accessed, challenging the traditional face-to-face approach while simultaneously facilitating the rise of online learning modalities [8].

The transition to online learning has emerged as a critical solution for addressing key issues in education stemming from the disruptions caused by COVID-19 [7]. Digital learning is regarded as a solution that enables the provision and access to education to overcome barriers associated with the reduction of social interactions in the context of distancing, which could adversely affect student learning experiences and performance [9]. This shift not only ensures continuous access to learning materials but also highlights the flexibility and resilience of digital platforms in maintaining educational processes in the face of unexpected disruptions [7]. Simultaneously, it underscores the necessity of developing and ensuring that educational systems are adaptable and capable of adjusting to changing circumstances.

Previous research during the pandemic primarily examined online education strategies, teaching facilitation, resources, policies, and the impact of lockdowns on student learning [10–12]. However, few studies compared factors affecting online learning experiences and satisfaction [6, 13]. The aspects that enhanced students’ satisfaction with online learning before the COVID-19 era were also understudied, making it challenging to determine satisfaction during the pandemic.

Gopal, Singh, and Aggarwal [14] noted insufficient research attention to students’ satisfaction and performance in online learning during COVID-19. Andersson and Grönlund [15] identified challenges in e-learning implementation in developed and developing countries, including learner, technology, course, and context dimensions. Developed countries faced fewer technology challenges due to advanced platforms. However, the attributes of e-learning that contribute to satisfaction during challenging times like COVID-19 remain unclear. Thus, our research question is as follows: What factors of online education determine students’ satisfaction across all universities in Vietnam during COVID-19? Our study objectives are (1) to identify factors influencing students’ satisfaction with online learning and (2) to examine their relationship with satisfaction among all university students during the pandemic.

To achieve our research goals, the technology acceptance model (TAM) is utilized as the theoretical model in this study. The TAM was proposed by Fred Davis in 1986, with the aim of explaining user behavior towards new technology based on user attitudes. The TAM shows its dominance in most research to examine the online learning satisfaction of undergraduates who experienced the COVID-19 pandemic period [16, 17]. This study focuses on Vietnamese undergraduate students’ online learning experiences across all universities. Data is collected from various faculties through Zoom and Google Meet during the COVID-19 pandemic using the user experience questionnaire (UEQ) and a quantitative methodology (forms). Results will highlight primary factors significantly influencing undergraduates’ satisfaction with online learning, different from previous studies (e.g., [6, 13]). The paper offers potential solutions for online learning challenges and extensive discussion on factors related to online education and satisfaction. The study approach and data collection methods are described, followed by the presentation and discussion of results, incorporating both theoretical implications and practical insights.

2. Research Concepts and Hypothesis Development

Prior research during the pandemic predominantly focused on strategies for online education, facilitation of teaching, resources, policies, and the impact of lockdowns on student learning [10–12]. Nonetheless, there has been limited comparative analysis of factors influencing online learning experiences and satisfaction [6, 13]. The exploration of the effects of study workloads, enhancing student engagement, and technical issues in online learning on student experiences constitutes three principal themes that previous research frequently concentrated on [6].

Among the notable factors explored in studies that could impact students’ satisfaction with online learning include lecturers’ competence and commitment, students’ technical abilities, technical self-efficacy, and adaptability, along with system and information quality. Lecturer competence refers to the instructors’ ability to deliver online content effectively, engage with students, and provide timely feedback via online platforms [8, 18], while students’ technical abilities pertain to the capacity, confidence, and belief of students in their ability to succeed in an online learning environment [4, 19]. Besides these intrinsic factors, an external factor that can affect students’ online learning experience is the technical support and system design, such as user-friendly interfaces, ease of navigation, and the availability of necessary resources [8]. Furthermore, constructing a support system that facilitates better communication among students or between students and instructors plays a crucial role in enhancing student engagement and satisfaction in online learning [6]. However, despite receiving significant attention, the aspects that enhanced students’ satisfaction with online learning before the COVID-19 era were also understudied, complicating the assessment of satisfaction during the pandemic. Moreover, previous studies focusing on this aspect were often conducted in developed countries such as China [8] and the United States [19, 20], while there has been a scarcity of research in developing countries like Vietnam, where students may exhibit different cultural traits, adaptability, and technological infrastructure compared to those in developed nations.

The strong shift towards online learning and education under the influence of COVID-19 makes comprehending the multifaceted factors influencing student satisfaction with online learning modalities has become paramount for educational institutions globally. This section focuses on exploring several pivotal determinants hypothesized to shape students’ online learning experiences, including satisfaction with online learning, instructors’ attitudes, students’ perceptions of online learning technology, interactions among students, online communication, the structure and content of online courses, outcomes, motivation for learning, and the impact of the abrupt transition from traditional to face-to-face classes to online formats. While most of these factors are expected to positively influence students’ satisfaction, the sudden shift from conventional classroom settings to online platforms is hypothesized to negatively affect their overall sense of learning contentment. Additionally, this section introduces a proposed research model, aimed at providing a detailed understanding of the interrelationships among these factors and their collective impact on students’ satisfaction with online learning during this global health crisis.

2.1. Online Learning Satisfaction

The COVID-19 outbreak has changed the world, and universities must switch from traditional learning methods to online learning methods to prevent the transmission of infectious diseases through social interaction [21, 22] and adverse impacts on student life, grades, and academic achievement [23]. Online learning means that both teachers and students converge in a virtual classroom environment to participate in educational activities from different places and at different times through an Internet connection [24]. Online learning allows instructors to update learning materials in different file formats and easily track learning progress and students’ learning results without having to go to class like lecture-format traditional teaching [25].

In this study, we posit that online learning satisfaction requires advanced teaching methods and technological know-how to capture students’ attention and deliver learning instruction [26, 27]. The level of effectiveness when learning online and the psychological state of students will affect student satisfaction in learning. Meanwhile, students’ expectations about the online learning system are very complicated due to the novel nature of this form of online learning [27]. Satisfaction with the teaching methods of lecturers also has a positive impact on student learning outcomes [28]. As such, fulfilling the student’s expectations and gaining positive views of the online learning system will then create online student satisfaction.

2.2. Technology

The efficiency of online learning depends on how users perceive the technology [29, 30]. Technical issues and lack of support can lead to frustration in online learning [31]. Users’ perception of the utility and usability of online learning technology plays a crucial role in its adoption [32]. Factors like visual design, navigation, and functionality also influence user engagement [33]. Users’ past knowledge and proficiency with the technology, as well as their perception of its quality, impact the success of online learning [34]. Computer self-efficacy is positively related to users’ intention to use technology for learning [32]. Past research supports the hypothesis that technology affects students’ online learning experiences [35–38].

H1. Students’ perception of online learning technology positively affects their online learning satisfaction.

2.3. Motivation in Learning

Motivation plays a crucial role in today’s student learning, representing a person’s internal drive to achieve goals [39]. It encompasses a student’s willingness, creativity, and engagement in classroom activities [40]. Studies have emphasized that maintaining student satisfaction in online classrooms is contingent on motivation [40]. Students with higher motivation tend to succeed more in online learning compared to those with lower motivation [27, 41]. Therefore, our hypothesis is based on the influence of “motivation for learning” as an essential factor in students’ online learning satisfaction.

H2. Motivation in learning positively affects students’ online learning satisfaction.

2.4. Communication

The success of remote learning relies on effective communication between teachers and students [29]. To combat potential feelings of isolation in online learning, instructors are advised to use interactive teaching techniques, encourage discussions, and provide multiple communication channels [30]. The interaction between learners and teachers within the context of online education is typically facilitated through mechanisms such as real-time feedback systems, learning-management systems, class discussions, emails, and phone calls [42–44]. Research indicates a favorable impact on students’ motivation to learn through the provision of support, known as scaffolding [45]. Notably, Borup, Graham, and Davies [46] observed that, among the three interaction types delineated by Moore [47], virtual high school students perceived learner–teacher interaction as the most advantageous for enhancing their motivation to learn. Additionally, Liu and Cavanaugh [48] identified a statistically significant positive correlation between the frequency of learner–teacher interactions and final scores in an online high school algebra course.

H3. Online communication positively affects students’ online learning satisfaction.

2.5. Instructor

Teachers and students in online learning significantly influence each other’s experiences [29]. Hartman, Dziuban, and Moskal [49] show a strong connection between instructor satisfaction and student learning. When teachers perform well, students are more content. Thus, factors such as a teacher’s methods and demeanor impact student motivation and involvement [34]. Effective use of online learning technology by instructors also affects students’ learning outcomes [33].

H4. Instructor’s attitude and performance positively affect students’ online learning satisfaction.

2.6. Course

Students’ satisfaction with online learning may depend on their confidence in learning the course material [50]. Subject-specific variations should be considered when evaluating the efficiency of online learning [51]. Courses that require practical information and skills might not be as effective online, impacting student learning and happiness. However, if the course material is well-suited for the online environment, students may prefer online enrollment. Cultural differences can also influence students’ perceptions of the benefits of online courses, with Asian students, for example, having different learning styles and preferences [52, 53].

H5. Online courses positively affect their online learning satisfaction.

2.7. Outcome

Research indicates that student satisfaction is closely connected to academic performance and participation [54]. Perceived learning outcomes also play a significant role in influencing student satisfaction with online learning [55]. By considering both student satisfaction and reported learning outcomes, we can better assess the effectiveness of online education [56]. Previous studies have emphasized a strong link between students’ satisfaction with online learning and their overall learning experience [57]. A satisfied student is considered a positive indication of effective learning, and actual student learning outcomes are a reliable indicator of satisfaction in online learning [58]. Hypothesis H6 proposes the following:

H6. Outcome positively affects their online learning satisfaction.

2.8. Sudden Change

The impact of external factors on teachers’ and students’ online teaching and learning experiences is being investigated, along with the virtual learning environment. The sudden shift to complete online learning due to the pandemic has caused significant disruptions in the global education sector [59]. Although educators and learners were unprepared for this upheaval, they had little choice but to adapt. Therefore, it is crucial to identify potential external elements that may affect teacher and student satisfaction in the virtual educational environment [29]. The hypothesis is that the rapid transition from traditional to online learning during the pandemic could adversely affect students’ teaching and learning experiences.

H7. The sudden change from traditional to online classes negatively affects students’ online learning satisfaction.

Based on various studies by authors such as Lei and So [60]; Al-Mawee, Kwayu, and Gharaibeh [61]; Rajeh et al. [62]; and Saravanan et al. [63], several factors are identified that influence students’ satisfaction with online learning during the COVID-19 pandemic. Figure 1 illustrates this study’s proposed research model which maps the influences of various factors, including the instructor, technology, communication, course, learning outcomes, motivation for learning, and sudden changes, on online learning satisfaction.

3. Methodology

This study is aimed at investigating the factors influencing students’ satisfaction with online learning during the COVID-19 pandemic and propose solutions for enhancing their learning experience. In this study, the UEQs are utilized to allow a quick assessment done by end users covering a preferably comprehensive impression of user experience [64]. In fact, we believe that this approach should allow the users to express feelings, impressions, and attitudes that arise when experiencing the service under investigation in a very simple and immediate way.

Measurement scales were adapted from existing scales found in previous research. First, an English version of the measures was developed by adapting items from previous studies. An independent translator helped translate the measures into Vietnamese, which was back-translated into English by another independent translator. The authors worked with these translators to resolve several back-translation ambiguities and ensure the translation accuracy. Second, the adapted items were subjected to a focus group discussion with several experts working in the higher education of Vietnam to enhance the face validity. The items were then revised according to the focus group feedback. Third, a pilot study was conducted with 35 students in Vietnam to validate and further refine the measures.

Sampling was based on convenience and accessibility, with 448 students from various Vietnamese universities selected. After screening, 437 satisfactory samples were obtained from April 25 to April 28, 2022, using an online survey created on Google Forms for accuracy and safety amid the pandemic.

Following the definition of research objectives, subjects, and time, the study developed a scale, outlined in Table 1, utilizing a Likert 5-point scale (ranging from 1: completely disagree to 5: completely agree) to collect data.

4. Results

4.1. Assessment of Measurement Model

4.1.1. Descriptive Statistics for Demographic Variables

All the participants in the survey are undergraduate students in Vietnam who have participated in online learning during the time of social distancing because of the COVID-19 pandemic. Participants will have characteristics such as gender, year of a course of study, and city/province in which the university is situated. The results obtained after the survey are as follows.

According to Table 2, the survey respondents mainly consist of female students, representing 78.95% of the total, which is 3.75 times higher than male students, who make up only 21.05%. Among the participants, the majority are freshmen and sophomores. First-year students constitute the highest proportion at 42.33%, followed closely by second-year students at 41.65%. Fourth-year students account for an average of 9.15%, while third-year students have a lower representation of 5.95%. A few students from the fifth and sixth years also participated in the survey, each at a relatively similar rate of 0.5%.

4.1.2. Measurement Model Analysis (External Model)

Following Hair et al. [65], we first evaluate the validity and reliability results of all items based on the outer loading value (> 0.7) and AVE value (> 0.5). Hock and Ringle [66] suggest that a scale achieves convergent value if the AVE is 0.5 or higher. This level of 0.5 (50%) means that the average parent latent variable will explain at least 50% of the variation of each observed variable. The results of the analysis are presented in Table 3 as follows.

From Table 3, as we can see, all the outer loadings of the items are above 0.7 and the AVE above 0.5. This result indicates that the measurement model meets the convergent validity requirements [65]. Next, we evaluate the reliability of the scale on SMARTPLS through two main indicators, Cronbach’s alpha and composite reliability (CR). Hair et al. [65] suggested that Cronbach’s alpha should be higher than 0.7 and CR must be higher than 0.7. We find that the Cronbach alpha of all constructs is higher than 0.7. In particular, the lowest Cronbach alpha value is 0.769 for technology components, while the highest value is 0.912 for components of motivation for learning. We also can find that all the constructs have a value of CR higher than 0.7, suggesting that the reliability of the constructs is suitable for further testing.

4.1.3. Discriminant Variable

We utilize the square root of AVE as proposed by Fornell and Larcker [67] and the HTMT index [68] to evaluate the discriminant value. These results are presented in Tables 4 and 5.

The results of Fornell–Larcker in Table 4 revealed that the square root of AVE of the constructs exceeded the value of estimated correlations of a construct with other latent variables of the study [65], thus confirming the discriminate validity of the constructs. Therefore, with all the statistical criteria met, the validity and the reliability of the reflective measurement model were established for our results.

4.1.4. Structural Model Analysis (Inner Model)

In this part, we provide the results of the path coefficient and -statistics of our research model by using the PLS-SEM algorithms. The Bootstrapping interface (with , two-tailed testing, and a significance level of 0.05) was utilized to verify the statistical significance of the path coefficients [65]. The results are presented in Table 6 and Figure 2.

From Table 6, it could be seen that hypotheses H1 (CN ➔ HL), H2 (DL ➔ HL), H3 (GT ➔ HL), H5 (KH ➔ HL), H6 (KQ ➔ HL) were significant because the -value is significant at . However, we find that H4 (GV ➔ HL) and H7 (TD ➔ HL) are statistically insignificant with . Thus, the hypotheses H4 and H7 are not accepted.

Within the squared adjusted, according to Hair et al. [65], values of 0.75, 0.50, and 0.25 for the endogenous constructs could be interpreted, respectively, as substantial, moderate, and weak. The squared adjusted value is 0.602. Thus, it can be concluded that value for HL constructs could be considered to be close to substantial.

To measure the predictive power of the model, Stone [69] and Geisser [70] proposed the index out-of-sample predictive power. Tenenhaus et al. [71] stated that is considered an index to evaluate the overall quality of the component model. Accordingly, if all component models have , the overall structural model of the study also has overall quality. According to Hair et al. [65], the levels of corresponding to the predictive power of the model are as follows: , low forecast accuracy; , average forecast accuracy; , high level of forecast accuracy. The results are presented in Table 7.

From Table 7, we find that HL has of 0.418 (>0), indicating that the predictive power of the model is middle forecast accuracy.

Following Masudin et al. [72], we next evaluate the model fit results based on three statistical criteria, including, standardized root mean square residual (SRMR), the normed fit index (NFI), and the three fit models for bootstrapped-based statistics (d-ULS, d_G, and chi-square). The results are presented in Table 8.

Table 8 reveals that the NFI value is 0.819 (>50%), indicating that the model is fit because the model used in this research has a percentage of 81.9%. In addition, the SRMR value is 0.057 (<0.08), which indicates that the correlation matrix model is appropriate [73]. This result suggests that student online satisfaction has been successfully modeled.

5. Discussions

5.1. Theoretical Discussions and Implications

This study investigates the specific attributes of online learning and how those attributes affect students’ satisfaction with online educational environments during COVID-19. This study contributes valuable insights by knowing how students’ opinions of this new learning environment and how it affects their level of satisfaction with their education can help develop techniques that encourage ongoing involvement and make online learning more appealing. Our findings demonstrate that five out of seven attributes including technology, communication, course, outcome, and motivation for learning are statistically significant on online learning satisfaction, while instructor and sudden change are not.

First, technology is confirmed as a critical attribute to enhance online learning satisfaction. This observation further reinforces prior findings by Mahmood [74], which posited that the technology infrastructure plays a pivotal role in facilitating access to learning materials and significantly influences the students’ online learning experience. In fact, the result shows that applying technologies positively impacts online learning satisfaction by supporting students in their learning process, such as easy access to learning materials, including online resources, online forums, and lecture slides, tend to perform better in their studies. This result is consistent with Twigg [75] and Adeyinka-ojo and Ikumoro [76]. This result can be explained in that the students’ satisfaction can be ensured by encouraging active learning in a digital teaching environment with a variety of educational materials. Therefore, technological factors can mitigate the disruptions caused by COVID-19, when educational institutions use online platforms to generate useful, comprehensible, and visually appealing virtual content that encourages satisfaction and continued engagement in online learning [77].

Second, the result shows that online communication positively affects online learning satisfaction. A detailed analysis of this paper has revealed that online learning helps students boost their confidence in communicating with other students and instructors in activities to exchange and contribute ideas [78]. This result is in line with the several previous studies. For instance, Rodriguez, Ooms, and Montañez [79] show that for students with online course experience, comfort or confidence had a strong positive connection with satisfaction. Furthermore, this study also reinforces the assertions made by Chakraborty et al. [80], Mahmood [74], and Toquero and Talidong [81], which argue that communication and interaction with instructors and among students themselves play a crucial role in ensuring a positive online learning experience. This is identified as one of the most significant factors that students consider when engaging in online courses. This consistency suggests that the mode of communication may play a crucial role in shaping student satisfaction. Therefore, fostering meaningful interactions and effective communication between students and teachers in online settings is crucial for enhancing satisfaction [78, 82]. Moreover, by emphasizing the role of communication in students’ online learning satisfaction, the findings from this study also reinforce the call to action by Chakraborty et al. [80], who argue that while online education is considered a viable alternative during the pandemic, there is room for improvement in terms of interaction. Furthermore, in line with the observations of Skulmowski and Rey [83], who emphasize the role of deploying hybrid communication models, incorporating both online and in-person interactions to promote student satisfaction, the results of this study consistently recognize the value of online interactions in the current educational landscape, suggesting that with proper strategies and tools, online communication can effectively contribute to a satisfying learning experience. Additionally, instructors can easily promptly address student inquiries and encourage student interactions [84].

Third, the result shows that there is a positive and significant relationship between the course and online learning satisfaction. Particularly, the result reveals that online learning satisfaction comes from the online courses’ valuable information, instructions, and easy-to-use. This result is in line with the findings of Kauffman [85]. Kauffman [85] indicates that the integrated course design model (online discussions, guidelines provided by the instructor) can increase the level of online learning satisfaction. This result also provides further support for the findings from Chakraborty et al. [80], which highlighted that the delivery of course content significantly positively affects students’ online learning experience. Additionally, this outcome reinforces the observations made by Bao [10], which emphasized the importance of dividing content into smaller units to aid student focus, recognizing the tendency of online learners to have shorter attention spans. Furthermore, learners’ preferences for traditional or online classes may vary [86], highlighting the importance of catering to individual learning styles. Thus, a well-balanced interaction that is customized to the preferences of specific learner groups can be incorporated into online course designs to support student learning and satisfaction as well as their social integration [84].

Fourth, the results indicate a positive correlation between learning outcomes and satisfaction in online learning. This correlation affirms the importance of designing online learning environments that prioritize efficiency, align with learner expectations, promote practical application, and foster a sense of accomplishment, ultimately enhancing overall satisfaction and learning success. This result is in line with the findings of Means et al. [87], Boling et al. [78], and Artino [88]. Furthermore, the ability to apply learned concepts to real-world scenarios, a key determinant of successful learning outcomes [78], is emphasized. Means et al. [87] suggested that instructors should incorporate a practical orientation in online learning to facilitate the transfer of skills to real-world contexts, thereby enhancing the relevance of acquired knowledge. Satisfaction with personal performance is often linked to positive learning outcomes, as it signifies a sense of mastery and accomplishment [88]. Additionally, this finding further corroborates the assertion by Chakraborty et al. [80], which identifies the significant impact of online assessment design on student satisfaction. Thus, instructors should support self-directed learning, which is a common feature of online education that contributes to a positive learning experience and a sense of accomplishment [89].

Finally, there is a positive relationship between motivation and online learning satisfaction. It means that maintaining learners’ satisfaction with online learning necessitates a deliberate effort to increase their motivation for studying. According to Croxton [84], instructors can foster acquaintance and friendship among students, motivating them to engage actively with peers and instructors. Thus, instructors should be offered many learning options within each course which helps to keep their interest in the online course content [75]. He also mentions that instructors should be flexible and create environments where students are able to choose online courses that provide their necessary knowledge, which has long-term benefits.

5.2. Practical Discussion and Implications

Emphasizing the role of students’ perceptions of online learning technology in enhancing satisfaction with online learning, this research underscores the necessity for educators to integrate various educational technologies to support the learning process. This includes the utilization of learning management systems, online forums, and multimedia resources to facilitate easy access to learning materials. This suggestion aligns with Toquero and Talidong [81], who highlighted the role of integrating social media into online learning platforms to enhance communication capabilities and serve as supplementary tools for instruction and information dissemination. Additionally, the development or integration of supportive communication platforms within online learning platforms is another aspect that educators need to consider, based on findings confirming the significant impact of communication on students’ online learning satisfaction. Furthermore, according to this study’s findings, the incorporation of findings regarding the significant effects of online communication and course design suggests that educators should encourage active participation in online discussions and provide diverse platforms for communication, such as video conferences, discussion boards, and social media groups, to enhance students’ online learning experiences. This suggestion corresponds with recommendations from Skulmowski and Rey [83] and Chakraborty et al. [80], which underscored the necessity for universities to expand their digital infrastructure. This expansion includes adopting video conferencing tools, significantly increasing the number of classes that offer video-based learning content, and incorporating slideshows, note-taking programs, and specialized online tools for problem-solving, programming, and designing to enrich courses. Implementing communication activities and creating a sense of community also helps maintain student engagement and keep students motivated in an online environment [90], which are significant factors determining students’ online learning satisfaction, according to findings from this study.

The findings of this study, which indicate a nonsignificant relationship between instructors’ attitudes and the abrupt transition from traditional to online classes on students’ satisfaction with online learning, suggest an important implication that students currently possess the capability and confidence in their self-learning abilities to adequately engage in online educational activities. They are not dependent on instructors’ guidance to adapt to new learning modalities but are capable of self-exploration and discovery. Therefore, with students’ confidence in their ability to self-explore, the development of user-friendly learning systems plays a crucial role in encouraging their desire to explore and thereby enhance the personalized education experience through online learning formats.

6. Conclusions

This study investigates factors influencing online learning satisfaction among Vietnamese university students during the COVID-19 pandemic. Thirty-one observed variables representing seven contributing factors, namely, instructor, technology, communication, course, outcome, motivation for learning, and sudden change, were examined. Notably, technology, communication, course, outcome, and motivation for learning have a significant impact on online learning satisfaction.

To enhance students’ online learning satisfaction, instructors should promptly address student inquiries, encourage student interactions, and ensure fairness in grading. Teachers can foster acquaintance and friendship among students, motivating them to engage actively with peers and instructors. Moreover, offering a diverse range of online materials and preferences to meet students’ needs can improve their satisfaction with online education. However, students’ preferences for traditional or online classes may vary [86], highlighting the importance of catering to individual learning styles. Additionally, fostering meaningful interactions and effective communication between students and teachers in online settings is crucial for satisfaction [78, 82].

The study’s limitations include the influence of the COVID-19 pandemic on students’ perceptions, potential cultural context restrictions due to the sample being limited to Vietnam, reliance on self-report data with possible biases, exclusion of students with technological challenges, omission of other relevant variables, and the cross-sectional design’s inability to capture longitudinal trends.

Each country may have distinct perspectives, cultures, adaptability levels, and technological infrastructures; hence, the factors influencing and their potential impact on students’ online learning experiences can vary between countries. Future research could continue to explore the factors affecting students’ online learning experiences in other developing countries and compare them with the findings of this study and previous scholars to determine whether the differences that culture, adaptability, and technological infrastructure can introduce to the elements contributing to students’ satisfaction. Additionally, student satisfaction can be a subjective concept, and there may be significant variances among individuals. Therefore, to gain a deeper understanding of satisfaction and the factors contributing to students’ contentment with online learning courses, implementing qualitative methods might be necessary and promises to provide further significant insights into this research aspect. Moreover, student satisfaction often results from a learning process. Furthermore, the development and adoption of online learning have been underway for an extended period. Consequently, future studies, instead of applying a cross-sectional approach, could adopt a longitudinal approach to explore changes in student satisfaction with online learning and the factors affecting it through different periods, including before, during, and after COVID-19. This promises to offer a more comprehensive view of this research issue and contribute significantly to both theoretical and practical aspects.

Data Availability Statement

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

Funding

This research is partly funded by the University of Danang, University of Economics, Vietnam.

Acknowledgments

We thank the editor and anonymous referees for their comments and suggestions. This research is partly funded by the University of Danang, University of Economics, Vietnam.

References

Worldometer, “Coronavirus cases: statistics and charts,” 2020, Query 28th June 2022, from https://www.worldometers.info/coronavirus/coronavirus-cases/.
View at: Google Scholar
WHO, “COVID-19: IFRC, UNICEF and WHO issue guidance to protect children and support safe school operations,” 2020, Query 28th June 2022, from https://www.who.int/news/item/10-03-2020-covid-19-ifrc-unicef-and-who-issue-guidance-to-protect-children-and-support-safe-school-operations.
View at: Google Scholar
P. Kumar, C. Saxena, and H. Baber, “Learner-content interaction in e-learning-the moderating role of perceived harm of COVID-19 in assessing the satisfaction of learners,” Smart Learning Environments, vol. 8, pp. 1–15, 2019.
View at: Publisher Site | Google Scholar
D. U. N. Ranadewa, T. Y. Gregory, D. N. Boralugoda, J. A. H. T. Silva, and N. A. Jayasuriya, “Learners’ satisfaction and commitment towards online learning during COVID-19: a concept paper,” Vision, vol. 27, no. 5, pp. 582–592, 2023.
View at: Publisher Site | Google Scholar
M. J. Bundick, R. J. Quaglia, M. J. Corso, and D. E. Haywood, “Promoting student engagement in the classroom,” Teachers College Record, vol. 116, no. 4, pp. 1–34, 2014.
View at: Publisher Site | Google Scholar
W. Elshami, M. H. Taha, M. Abuzaid, C. Saravanan, S. Al Kawas, and M. E. Abdalla, “Satisfaction with online learning in the new normal: perspective of students and faculty at medical and health sciences colleges,” Medical Education Online, vol. 26, no. 1, article 1920090, 2021.
View at: Publisher Site | Google Scholar
I. Bartusevičienė, A. Pazaver, and M. Kitada, “Building a resilient university: ensuring academic continuity—transition from face-to-face to online in the COVID-19 pandemic,” WMU Journal of Maritime Affairs, vol. 20, no. 2, pp. 151–172, 2021.
View at: Publisher Site | Google Scholar
N. Yu and X. Liu, “Online dance learning satisfaction after the pandemic: lessons from the crisis,” SAGE Open, vol. 14, no. 1, article 21582440241227267, 2024.
View at: Publisher Site | Google Scholar
C. Son, S. Hegde, A. Smith, X. Wang, and F. Sasangohar, “Effects of COVID-19 on college students’ mental health in the United States: interview survey study,” Journal of Medical Internet Research, vol. 22, no. 9, article e21279, 2020.
View at: Publisher Site | Google Scholar
W. Bao, “COVID-19 and online teaching in higher education: a case study of Peking University,” Human Behavior and Emerging Technologies, vol. 2, no. 2, pp. 113–115, 2020.
View at: Publisher Site | Google Scholar
R. Huang, A. Tlili, T.-W. Chang, X. Zhang, F. Nascimbeni, and D. Burgos, “Disrupted classes, undisrupted learning during COVID-19 outbreak in China: application of open educational practices and resources,” Smart Learning Environments, vol. 7, no. 1, p. 19, 2020.
View at: Publisher Site | Google Scholar
C. Rapanta, L. Botturi, P. Goodyear, L. Guàrdia, and M. Koole, “Online university teaching during and after the Covid-19 crisis: refocusing teacher presence and learning activity,” Postdigital Science and Education, vol. 2, no. 3, pp. 923–945, 2020.
View at: Publisher Site | Google Scholar
I. M. K. Ho, K. Y. Cheong, and A. Weldon, “Predicting student satisfaction of emergency remote learning in higher education during COVID-19 using machine learning techniques,” PLoS One, vol. 16, no. 4, article e0249423, 2021.
View at: Publisher Site | Google Scholar
R. Gopal, V. Singh, and A. Aggarwal, “Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19,” Education and Information Technologies, vol. 26, no. 6, pp. 6923–6947, 2021.
View at: Publisher Site | Google Scholar
A. Andersson and Å. Grönlund, “A conceptual framework for E-learning in developing countries: a critical review of research challenges,” The Electronic Journal of Information Systems in Developing Countries, vol. 38, no. 1, pp. 1–16, 2009.
View at: Publisher Site | Google Scholar
J. Drennan, J. Kennedy, and A. Pisarski, “Factors affecting student attitudes toward flexible online learning in management education,” The Journal of Educational Research, vol. 98, no. 6, pp. 331–338, 2005.
View at: Publisher Site | Google Scholar
N. S. A. Rani, Z. U. R. I. N. A. H. Suradi, and N. H. Yusoff, “An analysis of technology acceptance model, learning management system attributes, e-satisfaction, and e-retention,” International Review of Management and Business Research, vol. 3, no. 4, pp. 1984–1996, 2014, researchgate.net.
View at: Google Scholar
L. Chitkushev, I. Vodenska, and T. Zlateva, “Digital learning impact factors: student satisfaction and performance in online courses,” International Journal of Information and Education Technology, vol. 4, no. 4, pp. 356–359, 2014.
View at: Publisher Site | Google Scholar
K. A. Alshare, R. D. Freeze, P. L. Lane, and H. J. Wen, “The impacts of system and human factors on online learning systems use and learner satisfaction,” Decision Sciences Journal of Innovative Education, vol. 9, no. 3, pp. 437–461, 2011.
View at: Publisher Site | Google Scholar
H. Tseng, Y. C. Kuo, H. T. Yeh, and Y. Tang, “Relationships between connectedness, performance proficiency, satisfaction, and online learning continuance,” Online Learning, vol. 26, no. 1, pp. 285–301, 2022.
View at: Publisher Site | Google Scholar
S. W. Li, Y. Wang, Y. Y. Yang, X. M. Lei, and Y. F. Yang, “Comparison of prevalence and associated factors of anxiety and depression among people affected by versus people unaffected by quarantine during the COVID-19 epidemic in southwestern China,” Chinese Journal of Child Health, vol. 26, no. 3, article e924609, 2020.
View at: Publisher Site | Google Scholar
C. Wang, P. W. Horby, F. G. Hayden, and G. F. Gao, “A novel coronavirus outbreak of global health concern,” The Lancet, vol. 395, no. 10223, pp. 470–473, 2020.
View at: Publisher Site | Google Scholar
K. K. Naji, X. Du, F. Tarlochan, U. Ebead, M. A. Hasan, and A. K. Al-Ali, “Engineering students’ readiness to transition to emergency online learning in response to COVID-19: case of Qatar,” Eurasia Journal of Mathematics, Science and Technology Education, vol. 16, no. 10, article em1886, 2020.
View at: Publisher Site | Google Scholar
D. Gomezelj and Ž. Čivre, “Tourism graduate students’ satisfaction with online learning,” Tourism: An International Interdisciplinary Journal, vol. 60, no. 2, pp. 159–174, 2012, https://hrcak.srce.hr/84561.
View at: Google Scholar
Y. Zolotov, A. Reznik, S. Bender, and R. Isralowitz, “COVID-19 fear, mental health, and substance use among Israeli University students,” International Journal of Mental Health and Addiction, vol. 20, no. 1, pp. 230–236, 2022.
View at: Publisher Site | Google Scholar
H. Baber, “Determinants of students’ perceived learning outcome and satisfaction in online learning during the pandemic of COVID19,” Journal of Education and E-Learning Research, vol. 7, no. 3, pp. 285–292, 2020.
View at: Publisher Site | Google Scholar
H.-C. K. Hsu, C. V. Wang, and C. Levesque-Bristol, “Reexamining the impact of self-determination theory on learning outcomes in the online learning environment,” Education and Information Technologies, vol. 24, no. 3, pp. 2159–2174, 2019.
View at: Publisher Site | Google Scholar
S. C. Cobb, “Social presence and online learning: a current view from a research perspective,” Journal of Interactive Online Learning, vol. 8, no. 3, 2009.
View at: Google Scholar
D. U. Bolliger and O. Wasilik, “Factors influencing faculty satisfaction with online teaching and learning in higher education,” Distance Education, vol. 30, no. 1, pp. 103–116, 2009.
View at: Publisher Site | Google Scholar
T. Volery and D. Lord, “Critical success factors in online education,” International Journal of Educational Management, vol. 14, no. 5, pp. 216–223, 2000.
View at: Publisher Site | Google Scholar
D. E. Leidner and S. L. Jarvenpaa, “The information age confronts education: case studies on electronic classrooms,” Information Systems Research, vol. 4, no. 1, pp. 24–54, 1993.
View at: Publisher Site | Google Scholar
A. H. K. Yuen and W. W. K. Ma, “Exploring teacher acceptance of e-learning technology,” Asia-Pacific Journal of Teacher Education, vol. 36, no. 3, pp. 229–243, 2008.
View at: Publisher Site | Google Scholar
M. Sigala, “Investigating the factors determining e-learning effectiveness in tourism and hospitality education,” Journal of Hospitality and Tourism Education, vol. 16, no. 2, pp. 11–21, 2004.
View at: Publisher Site | Google Scholar
M. Hammond, “Communication within on-line forums: the opportunities, the constraints and the value of a communicative approach,” Computers & Education, vol. 35, no. 4, pp. 251–262, 2000.
View at: Publisher Site | Google Scholar
N. Almusharraf and S. Khahro, “Students satisfaction with online learning experiences during the COVID-19 pandemic,” International Journal of Emerging Technologies in Learning (iJET), vol. 15, no. 21, pp. 246–267, 2020.
View at: Publisher Site | Google Scholar
E. Lengetti, M. A. Cantrell, N. DellaCroce, L. Diewald, J. L. Mensinger, and R. Shenkman, “Learning environment and evidence among professionals and students satisfaction (LEAPS), experienced during the COVID-19 pandemic,” Teaching and Learning in Nursing, vol. 16, no. 4, pp. 342–346, 2021.
View at: Publisher Site | Google Scholar
M. Prodanović and V. Gavranović, “Students’ satisfaction with learning experience in Covid-19 imposed virtual learning environment,” Revista Publicando, vol. 8, no. 29, pp. 124–131, 2021.
View at: Publisher Site | Google Scholar
N. A. N. Shaid, F. M. Kamruzaman, and N. A. Sulaiman, “Online learning during ongoing Covid-19 pandemic: a survey of students’ satisfaction,” International Journal of Academic Research in Business and Social Sciences, vol. 11, no. 7, pp. 924–937, 2021.
View at: Publisher Site | Google Scholar
E. Harmon-Jones, C. Harmon-Jones, and T. F. Price, “What is approach motivation?” Emotion Review, vol. 5, no. 3, pp. 291–295, 2013.
View at: Publisher Site | Google Scholar
D. U. Bolliger, S. Supanakorn, and C. Boggs, “Impact of podcasting on student motivation in the online learning environment,” Computers & Education, vol. 55, no. 2, pp. 714–722, 2010.
View at: Publisher Site | Google Scholar
M. K. Barbour and T. C. Reeves, “The reality of virtual schools: a review of the literature,” Computers & Education, vol. 52, no. 2, pp. 402–416, 2009.
View at: Publisher Site | Google Scholar
Y. Beldarrain, Engaging the 21 st century learner: an exploratory study of the relationship between interaction and achievement in the virtual high school, [Ph.D. thesis], Capella University, 2008, https://www.proquest.com/openview/034a102e376cfcba26fbd77d684c4e79/1?pq-origsite=gscholar%2526cbl=18750.
A. Hawkins, M. K. Barbour, and C. R. Graham, “Strictly business: teacher perceptions of interaction in virtual schooling,” in In Society for Information Technology & Teacher Education International Conference, pp. 1520–1523, Association for the Advancement of computing in education (AACE), 2011, https://www.learntechlib.org/primary/p/36511/.
View at: Google Scholar
M. Barbour and C. Plough, “Helping to make online learning less isolating,” TechTrends, vol. 53, no. 4, p. 57, 2009.
View at: Google Scholar
E. Murphy and M. A. Rodríguez-Manzanares, “Teachers’ perspectives on motivation in high-school distance education,” International Journal of E-Learning & Distance Education, vol. 23, no. 3, pp. 1–24, 2009, https://ijede.ca/index.php/jde/article/view/602.
View at: Google Scholar
J. Borup, C. R. Graham, and R. S. Davies, “The nature of adolescent learner interaction in a virtual high school setting,” Journal of Computer Assisted Learning, vol. 29, no. 2, pp. 153–167, 2013.
View at: Publisher Site | Google Scholar
M. G. Moore, “Editorial: three types of interaction,” The American Journal of Distance Education, vol. 3, no. 2, pp. 1–7, 1989.
View at: Publisher Site | Google Scholar
F. Liu and C. Cavanaugh, “Factors influencing student academic performance in online high school algebra,” Open Learning: The Journal of Open, Distance and e-Learning, vol. 27, no. 2, pp. 149–167, 2012.
View at: Publisher Site | Google Scholar
J. Hartman, C. Dziuban, and P. Moskal, “Faculty satisfaction in ALNs: a dependent or independent variable,” Journal of Asynchronous Learning Networks, vol. 4, no. 3, pp. 155–179, 2000.
View at: Publisher Site | Google Scholar
M. F. Fortune, M. Spielman, and D. T. Pangelinan, “Students’ perceptions of online or face-to-face learning and social media in hospitality, recreation and tourism,” MERLOT Journal of Online Learning and Teaching, vol. 7, no. 1, pp. 1–16, 2011, https://jolt.merlot.org/vol7no1/fortune_0311.htm.
View at: Google Scholar
T. Sitzmann, K. Kraiger, D. Stewart, and R. Wisher, “The comparative effectiveness of web-based and classroom instruction: a meta-analysis,” Personnel Psychology, vol. 59, no. 3, pp. 623–664, 2006.
View at: Publisher Site | Google Scholar
D. M. Baker and R. Unni, “USA and Asia hospitality & tourism students' perceptions and satisfaction with online learning versus traditional face-to-face instruction,” E-Journal of Business Education and Scholarship of Teaching, vol. 12, no. 2, pp. 40–54, 2018, https://eric.ed.gov/?id=EJ1193340.
View at: Google Scholar
C. Chin and D. E. Brown, “Learning in science: a comparison of deep and surface approaches,” Journal of Research in Science Teaching, vol. 37, no. 2, pp. 109–138, 2000.
View at: Publisher Site | Google Scholar
K. A. Meyer, “Student engagement in online learning: what works and why,” ASHE Higher Education Report, vol. 40, no. 6, pp. 1–114, 2014.
View at: Publisher Site | Google Scholar
R. B. Ikhsan, L. A. Saraswati, B. G. Muchardie, and A. Susilo, “The determinants of students' perceived learning outcomes and satisfaction in BINUS online learning,” in 2019 5th International Conference on New Media Studies (CONMEDIA), pp. 68–73, Bali, Indonesia, 2019.
View at: Publisher Site | Google Scholar
J. A. Gray and M. DiLoreto, “The effects of student engagement, student satisfaction, and perceived learning in online learning environments,” International Journal of Educational Leadership Preparation, vol. 11, no. 1, p. n1, 2016, https://eric.ed.gov/?id=EJ1103654.
View at: Google Scholar
L. C. Duque, “A framework for analysing higher education performance: students' satisfaction, perceived learning outcomes, and dropout intentions,” Total Quality Management & Business Excellence, vol. 25, no. 1-2, pp. 1–21, 2014.
View at: Publisher Site | Google Scholar
R. B. Marks, S. D. Sibley, and J. B. Arbaugh, “A structural equation model of predictors for effective online learning,” Journal of Management Education, vol. 29, no. 4, pp. 531–563, 2005.
View at: Publisher Site | Google Scholar
H. Natanson and V. Strauss, “America is about to start online learning, round 2. For millions of students, it won’t be any better. Washington Post,” 2020, https://www.washingtonpost.com/local/education/america-is-about-to-start-online-learning-round-2-for-millions-of-students-it-wont-be-any-better/2020/08/05/20aaabea-d1ae-11ea-8c55-61e7fa5e82ab_story.html.
View at: Google Scholar
S. I. Lei and A. S. I. So, “Online teaching and learning experiences during the COVID-19 pandemic – a comparison of teacher and student perceptions,” Journal of Hospitality & Tourism Education, vol. 33, no. 3, pp. 148–162, 2021.
View at: Publisher Site | Google Scholar
W. Al-Mawee, K. M. Kwayu, and T. Gharaibeh, “Student’s perspective on distance learning during COVID-19 pandemic: a case study of Western Michigan University, United States,” International Journal of Educational Research Open, vol. 2, article 100080, 2021.
View at: Publisher Site | Google Scholar
M. T. Rajeh, F. H. Abduljabbar, S. M. Alqahtani et al., “Students’ satisfaction and continued intention toward e-learning: a theory-based study,” Medical Education Online, vol. 26, no. 1, article 1961348, 2021.
View at: Publisher Site | Google Scholar
C. Saravanan, I. Mahmoud, W. Elshami, and M. H. Taha, “Knowledge, anxiety, fear, and psychological distress about COVID-19 among university students in the United Arab Emirates,” Frontiers in Psychiatry, vol. 11, article 582189, 2020.
View at: Publisher Site | Google Scholar
M. Schrepp, A. Hinderks, and J. Thomaschewski, “Design and evaluation of a short version of the user experience questionnaire (UEQ-S),” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 4, no. 6, pp. 103–108, 2017.
View at: Publisher Site | Google Scholar
J. F. J. Hair, G. T. M. Hult, C. M. Ringle, and M. Sarstedt, A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), Sage, Thousand Oaks, CA, 2nd edition, 2017, https://www.amazon.de/Partial-Squares-Structural-Equation-Modeling/dp/148337744X/ref=sr_1_1?ie=UTF8%2526qid=1462617386%2526sr=8-1%2526keywords=PLS-sem#reader_148337744X.
C. Hock and C. M. Ringle, “Local strategic networks in the software industry: An empirical analysis of the value continuum,” International Journal of Knowledge Management Studies, vol. 4, no. 2, pp. 132–151, 2010.
View at: Publisher Site | Google Scholar
C. G. Fornell and D. F. Larcker, “Evaluating structural equation models with unobservable variables and measurement error,” Journal of Marketing Research, vol. 18, no. 1, pp. 39–50, 1981.
View at: Publisher Site | Google Scholar
J. Henseler, C. M. Ringle, and M. Sarstedt, “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” Journal of the academy of marketing science, vol. 43, no. 1, pp. 115–135, 2015.
View at: Publisher Site | Google Scholar
M. Stone, “Cross-validation and multinomial prediction,” Biometrika, vol. 61, no. 3, pp. 509–515, 1974.
View at: Google Scholar
S. Geisser, “A predictive approach to the random effect model,” Biometrika, vol. 61, no. 1, pp. 101–107, 1974.
View at: Publisher Site | Google Scholar
M. Tenenhaus, V. Esposito Vinzi, Y.-M. Chatelin, and C. Lauro, “PLS path modeling,” Computational Statistics and Data Analysis, vol. 48, no. 1, pp. 159–205, 2005.
View at: Publisher Site | Google Scholar
I. Masudin, D. P. Restuputri, T. R. Indriani, E. Lau, and W. Widayat, “Modified-Kansei engineering for the quality of logistics services during the Covid-19 pandemic: Evidence from Indonesia,” Cogent Engineering, vol. 9, no. 1, p. 2064588, 2022.
View at: Publisher Site | Google Scholar
L. Hu and P. M. Bentler, “Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives,” Structural Equation Modeling: A Multidisciplinary Journal, vol. 6, no. 1, pp. 1–55, 1999.
View at: Publisher Site | Google Scholar
S. Mahmood, “Instructional strategies for online teaching in COVID‐19 pandemic,” Human Behavior and Emerging Technologies, vol. 3, no. 1, pp. 199–203, 2021.
View at: Publisher Site | Google Scholar
C. A. Twigg, “Improving learning and reducing costs for online learning,” in Encyclopedia of Distance Learning, pp. 1148–1154, IGI Global, Second edition, 2011.
View at: Publisher Site | Google Scholar
S. Adeyinka-ojo and A. O. Ikumoro, “COVID-19 pandemic and adoption of digital technology in learning and teaching,” in Proceedings of the 3rd International E-Conference on Advances in Engineering, Technology and Management, 2020.
View at: Publisher Site | Google Scholar
E. Agyeiwaah, F. Badu Baiden, E. Gamor, and F. C. Hsu, “Determining the attributes that influence students’ online learning satisfaction during COVID-19 pandemic,” In Journal of Hospitality, Leisure, Sport and Tourism Education, vol. 30, article 100364, 2022.
View at: Publisher Site | Google Scholar
E. C. Boling, M. Hough, H. Krinsky, H. Saleem, and M. Stevens, “Cutting the distance in distance education: perspectives on what promotes positive, online learning experiences,” The Internet and Higher Education, vol. 15, no. 2, pp. 118–126, 2012.
View at: Publisher Site | Google Scholar
M. C. Rodriguez, A. Ooms, and M. Montañez, “Students’ perceptions of online-learning quality given comfort, motivation, satisfaction, and experience,” Journal of Interactive Online Learning, vol. 7, no. 2, pp. 105–125, 2008.
View at: Google Scholar
P. Chakraborty, P. Mittal, M. S. Gupta, S. Yadav, and A. Arora, “Opinion of students on online education during the COVID‐19 pandemic,” Human Behavior and Emerging Technologies, vol. 3, no. 3, pp. 357–365, 2021.
View at: Publisher Site | Google Scholar
C. M. D. Toquero and K. J. B. Talidong, “Socio-educational implications of technology use during COVID‐19: a case study in General Santos City, Philippines,” Human Behavior and Emerging Technologies, vol. 3, no. 1, pp. 194–198, 2021.
View at: Publisher Site | Google Scholar
D. McConnell, EBOOK: E-Learning Groups and Communities, McGraw-Hill Education, UK, 2006.
A. Skulmowski and G. D. Rey, “COVID-19 as an accelerator for digitalization at a German university: establishing hybrid campuses in times of crisis,” Human Behavior and Emerging Technologies, vol. 2, no. 3, pp. 212–216, 2020.
View at: Publisher Site | Google Scholar
R. A. Croxton, “The role of interactivity in student satisfaction and persistence in online learning,” Journal of Online Learning and Teaching, vol. 10, no. 2, p. 314, 2014, https://jolt.merlot.org/vol10no2/croxton_0614.pdf.
View at: Google Scholar
H. Kauffman, “A review of predictive factors of student success in and satisfaction with online learning,” Research in Learning Technology, vol. 23, no. 1063519, pp. 1–13, 2015.
View at: Publisher Site | Google Scholar
B. W. Brown and C. E. Liedholm, “Can web courses replace the classroom in principles of microeconomics?” American Economic Review, vol. 92, no. 2, pp. 444–448, 2002.
View at: Publisher Site | Google Scholar
B. Means, Y. Toyama, R. Murphy, and M. Baki, “The effectiveness of online and blended learning: a meta-analysis of the empirical literature,” Teachers College Record, vol. 115, no. 3, pp. 1–47, 2013.
View at: Publisher Site | Google Scholar
A. R. Artino Jr., “Online or face-to-face learning? Exploring the personal factors that predict students' choice of instructional format,” The Internet and Higher Education, vol. 13, no. 4, pp. 272–276, 2010.
View at: Publisher Site | Google Scholar
M. Simonson, S. Smaldino, M. Albright, and S. Zvacek, Teaching and Learning at a Distance: Foundations of Distance Education, Information Age Publishing, Charlotte, NC, 2019, https://core.ac.uk/reader/84320820.
P. Shea, C. S. Li, and A. Pickett, “A study of teaching presence and student sense of learning community in fully online and web-enhanced college courses,” The Internet and Higher Education, vol. 9, no. 3, pp. 175–190, 2006.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2024 Le Phuoc Thanh et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

PDF Download Citation

Download other formats

Order printed copies

Views

104

Downloads

67

Citations