Introduction
Background
Education is essential for a country's comprehensive development, serving as a foundation for societal, economic, and political growth Modern societies demand lifelong, continuous learning, emphasizing self-learning and professional skill enhancement to improve quality of life E-learning has become a vital method to meet these educational needs effectively In Vietnam, the government and Party prioritize building a learning society, with the 2011 Communist Party Congress explicitly aiming to promote nationwide study encouragement, develop a learning culture, and expand e-learning initiatives.
Vietnam’s move toward a market-oriented economy and accession to the World Trade Organization (WTO) have created significant opportunities for Vietnamese businesses, including access to new markets, imported raw materials, advanced technologies, and increased international cooperation However, this openness also brings challenges such as heightened competition and stricter business standards related to product quality and safety One of the key difficulties faced by Vietnam is a lack of knowledge in business management, which hampers the development of a skilled workforce To address this, Vietnamese universities and colleges continuously strive to produce qualified business graduates who meet labor market demands, recognizing the importance of updating knowledge to remain competitive and fulfill the needs of both students and the economy.
Under Vietnam's 2012 education laws, higher education encompasses both undergraduate and postgraduate studies, with master's and doctoral programs classified as postgraduate Undergraduate programs typically lead to diplomas and bachelor's degrees, serving as the foundation of higher education The Vietnamese higher education system comprises universities, research institutions, and colleges, forming a diverse and comprehensive academic landscape.
Vietnam’s higher education system has evolved significantly over the past 11 years, with universities offering undergraduate, master’s, and doctoral programs as designated by the Prime Minister, and research institutions primarily providing doctoral programs Collaborating institutions can offer master’s programs with approval from the government Unlike universities, colleges mainly provide college degrees and lower-level undergraduate programs Currently, Vietnam has approximately 386 universities and colleges, reflecting a diverse and expanding tertiary education landscape (Nguyen, Oliver, and Priddy, 2009) Technological advancements and internet connectivity have made higher education more accessible through online learning and e-education platforms, transforming how students approach university courses Online education has garnered considerable interest for its potential to broaden access, enhance learning flexibility, and improve competitiveness within the education sector (Poehlein, 1996) The rapid growth of internet technology, coupled with a focus on lifelong learning and budget constraints, incentivizes universities to adopt e-learning methods across various disciplines, emphasizing the importance of embracing digital tools to stay competitive in a globalized education environment.
Research problem
E-learning is making use of technology innovations and Internet to deliver information for education and training (Sun, Tsai, Finger, Chen, & Yeh, 2006)
Thanks to advancements in information and communication technology, e-learning has become the modern paradigm of education It offers significant advantages, such as enabling flexible interactions between students and instructors or among students themselves, regardless of time and location, through both asynchronous and synchronous learning methods (Sun et al., 2006) These features make e-learning well-suited to meet the demands of a modern society's educational needs.
Many students at Vietnamese universities, such as the University of Science Ho Chi Minh City, Ho Chi Minh City Open University, Hutech University, Ho Chi Minh City University of Technology, and University of Information Technology, have expressed dissatisfaction and disappointment with their e-learning experiences Despite the shift to online education, these students often face challenges that impact their learning outcomes and overall satisfaction This indicates a need for improvements in the quality and delivery of e-learning programs across Vietnamese higher education institutions.
Research on student satisfaction is essential for determining whether colleges and universities are effectively fulfilling their mission, as a qualified graduate remains the primary outcome of educational institutions Satisfied students are more likely to be motivated, exert greater effort, and actively engage in their academic journey, including attending classes regularly and participating in coursework Studies (Bryant, 2006; Ozgungor, 2010) show that student satisfaction positively influences academic effort and commitment, highlighting its importance in higher education success.
Consequently, exploring the elements influencing the satisfaction of Vietnamese students is a need for academics and university administrators as well
This study aims to identify key factors influencing Vietnamese students' satisfaction in e-learning environments Specifically, it explores how various dimensions—such as instructor quality, learner engagement, and technological support—affect e-learner satisfaction Understanding these factors can help improve online education experiences for students in Vietnam.
Research plays a crucial role in education by enabling universities to understand the key factors influencing e-learner satisfaction The findings help institutions identify ways to enhance learner satisfaction and strengthen e-learning effectiveness Additionally, the study highlights the importance of integrating technological innovations into teaching and developing appropriate policies to improve instructors' training and teaching capabilities.
This study aims to identify the key factors that influence e-learners' satisfaction and evaluate the strength of each factor in enhancing student satisfaction with e-learning The primary objective is to determine which elements significantly impact learners' experiences, providing valuable insights into improving online education Ultimately, the research seeks to offer actionable recommendations to enhance e-learning effectiveness by understanding these critical satisfaction determinants.
This study explores the key factors influencing student satisfaction in e-learning environments It highlights the importance of interaction, learner attitudes towards technology, technology quality, and internet connectivity quality as critical determinants Understanding these elements can help improve online education experiences and enhance student engagement and satisfaction.
Ho Chi Minh City is one of the major business and education centers in Vietnam, so the empirical in this particular research is the e-learning in the context of
Ho Chi Minh city To be more specific, the population of this study is the e-learning students from Ho Chi Minh In other words, the data collected from the universities in the city and put into the analysis The last point needs to be mentioned in the research scope is that the object of study and survey is focused on the post-graduate students, and the graduated students; others types of students in the university are not considered in the thesis
The paper’s organization is constructed in five parts The first is the introduction of the study The second is the literature review and hypothesis The methodology is the following part The next part is the research results The final one is discussions, implications, limitations, and conclusion
Chapter 1 – Introduction This chapter reflects the current situation of education in Vietnam, as well as discusses about the existing researches in e-learning As a result, it leads to propose the research problem, research objectives and significance of this study also presented in this section
Chapter 2 – Literature review and hypothesis
This chapter establishes the theoretical foundation of the research by defining key concepts such as instructor capability and learner attitude towards graduation download processes It explores these concepts within their relevant context and reviews existing literature to demonstrate their relationships Based on this analysis, the chapter formulates and proposes hypotheses to guide the research.
Chapter 3 – Methodology There is no doubt that chapter 3 describes the way of establishment of the measures and conducting the survey This part includes two steps, qualitative research to modify draft measurement scale and quantitative research design to test the hypotheses
Chapter 4 – Research results The findings of this research are showed in this chapter The results are exhibited corresponding to each step of the data analysis As a result, the research hypotheses are tested
Chapter 5 – Discussions, Implications, Conclusion, and Limitations The last chapter of this study discusses the research results by affirming the exploratory values as well as connecting to the realistic conditions to suggest the practical application Limitations in the chapter are recognized in order to direct for further research in the future Finally, the generalization about e-learning is performed in conclusion tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Organization of the thesis
This article is structured into five comprehensive sections It begins with an introduction that outlines the study's purpose and significance The second section reviews relevant literature and presents the research hypotheses The methodology section details the research design and data collection procedures The fourth part reports the research results, while the final section discusses key findings, implications, limitations, and conclusions This organized framework ensures a clear understanding of the study’s objectives and outcomes, enhancing SEO with relevant keywords like "research study," "literature review," "methodology," "research findings," and "conclusions."
Chapter 1 – Introduction This chapter reflects the current situation of education in Vietnam, as well as discusses about the existing researches in e-learning As a result, it leads to propose the research problem, research objectives and significance of this study also presented in this section
Chapter 2 – Literature review and hypothesis
This chapter establishes the theoretical foundation of the research by defining key concepts such as instructor capability and learner attitudes towards e-learning, and exploring their relationships as presented in existing literature These definitions and relationships form the basis for developing research hypotheses.
Chapter 3 – Methodology There is no doubt that chapter 3 describes the way of establishment of the measures and conducting the survey This part includes two steps, qualitative research to modify draft measurement scale and quantitative research design to test the hypotheses
Chapter 4 – Research results The findings of this research are showed in this chapter The results are exhibited corresponding to each step of the data analysis As a result, the research hypotheses are tested
Chapter 5 – Discussions, Implications, Conclusion, and Limitations The last chapter of this study discusses the research results by affirming the exploratory values as well as connecting to the realistic conditions to suggest the practical application Limitations in the chapter are recognized in order to direct for further research in the future Finally, the generalization about e-learning is performed in conclusion tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Literature review and hypothesis
Elearning
E-learning represents the latest evolution in distance education, allowing learners and instructors to connect regardless of geographic location or time constraints It utilizes network technologies to create, deliver, and facilitate learning anytime and anywhere, making educational content accessible through web-based systems E-learning enables institutions to consistently deliver training, update content readily, reduce travel costs, and provide on-demand education for students globally Features such as live streaming lectures, electronic PowerPoint slides, and interactive message boards enhance the online learning experience Research from psychology and information systems highlights key factors influencing e-learning success, including models like the Technology Acceptance Model and the Expectation and Confirmation Model, which help understand user adoption and satisfaction.
Many articles have discussed about the benefits of e-learning (Liaw, Huang, &
E-learning offers numerous advantages, including the flexibility to choose when to attend lessons and the independence from lecturer-imposed schedules (Bouhnik & Marcus, 2006) It enables students to access online materials anytime and anywhere, promoting asynchronous interaction, which keeps discussions focused and efficient (Capper, 2001) Electronic messaging enhances group collaboration, allowing shared conversations and teamwork beyond physical meetings Additionally, online education introduces innovative teaching strategies, making diverse learning options more accessible and cost-effective, while providing learners opportunities to share their work and innovations with immediate support from electronic communities.
E-learning offers numerous advantages over traditional classroom training by providing a dynamic combination of visual, audio, and interactive learning methods, making education more effective for diverse learners It significantly reduces costs associated with printing, publishing, and distribution, while also saving expenses related to teacher salaries, classroom rentals, travel, and accommodation E-learning enables learners to control their pace, allowing customization of learning speed and focusing on relevant content, which enhances overall progress Additionally, it facilitates high-quality global faculty resource utilization, shortens learning time by 40-60%, and automates content delivery for consistent and accurate results For students, e-learning enhances flexibility, enabling them to study anytime and anywhere, accommodating overtime work or home-based learning, and improving their job readiness and skill development.
The expansion of information technology and communications has made online training a crucial tool for enabling self-study, self-improvement, and exam preparation, thereby addressing the diverse educational needs of a growing population Despite significant investments in expanding the quantity and quality of the school system, traditional education often cannot fully meet the learning demands of various learners Consequently, the Party promotes a learning movement that includes formal and non-formal education, aiming to create a learning society accessible to everyone (Tam, 2013) In response, the Education and Training sector has developed strategies emphasizing community mobilization and social learning opportunities, enabling lifelong learning for people of all abilities, ages, and locations, tailored to individual circumstances and contributing to human resource development (Tam, 2013) E-learning emerges as a vital training method that supports these initiatives by facilitating accessible, flexible, and personalized educational experiences.
Vietnam’s accession to the WTO and deeper integration into the global economy pose significant challenges for the country's education system, which must equip future citizens with sufficient skills, intelligence, and self-learning abilities to compete in an increasingly competitive environment E-learning has become a global phenomenon, with nearly 90% of Singaporean universities and over 80% of institutions in the USA adopting this method, according to Elearn (2010) The rapid development of information technology in Vietnam has led to a surge in Internet users, transforming how people work, study, and entertain themselves online Today, Vietnamese learners can access e-learning programs through three main channels: university-led courses, overseas programs introduced into Vietnam, and courses developed by private companies, reflecting the evolving digital education landscape.
The Vietnam Ministry of Education and Training has actively promoted integrating information technology into education by developing resources such as the e-learning website (el.edu.net) to facilitate access to online education Vietnam has adopted open-source software like Moodle to create and manage effective online learning systems, utilizing internationally recognized technologies like SCORM for content development and interoperability The Ministry has also implemented robust infrastructure, including fiber optic cables connected at 34 Mbps for international connectivity and 4 Mbps within the country, with support from Viettel, the national ICT provider, offering premium internet packages to educational institutions These initiatives demonstrate Vietnam's commitment to advancing digital learning through technology integration and infrastructure development.
E-learning in Vietnam currently lags behind developed countries like Singapore, Taiwan, and the USA, facing challenges such as low quality, limited scope, and insufficient participation from learners Additionally, the infrastructure necessary for effective online education is often lacking, which hampers the overall development The low level of online interaction between teachers and students, coupled with a predominantly cold and directive response style, further diminishes the effectiveness of e-learning Universities also struggle with a shortage of appropriate teaching methodologies and qualified staff, as some institutions prioritize rapid development over ensuring training quality Consequently, the true value of university-based e-learning remains underappreciated, presenting significant practical obstacles to its advancement.
As a consequence, many people doubt the quality of e-learning, and have e-learning dissatisfaction.
Student satisfaction
Student satisfaction, perception of quality, and self-confidence are fundamental concepts that are easy to understand However, extensive research efforts aim to clarify these ideas, develop reliable measurement tools, and explore how they influence each other as well as their impact on broader educational outcomes.
Consumer satisfaction is defined as the favorableness of an individual's subjective evaluation of the outcomes and experiences related to purchasing or using a product, as outlined by Hunt (1977) It reflects how a consumer perceives the overall experience based on personal judgment Similarly, Tse and Wilton (1988) describe customer satisfaction as the consumer's response to the perceived discrepancy between their prior expectations and the actual performance of the product after consumption These definitions highlight the importance of expectations, perceptions, and personal evaluation in determining customer satisfaction.
Student satisfaction in education reflects students’ subjective evaluations of their academic experiences and outcomes, significantly influenced by their overall campus life (Oliver & DeSarbo, 1989; Seymour, 1993) It is a dynamic measure, based on the students’ cumulative experiences within and outside the classroom, shaping their overall perception of the university (Oliver, 1980) According to Parasuraman et al (1985, 1988), satisfaction depends on the disconfirmation of expectations, occurring when perceived performance meets or exceeds expectations, while dissatisfaction arises from negative gaps between performance and anticipated outcomes.
Student satisfaction reflects a short-term attitude based on personal educational experiences, while perceived quality is a broader perception influenced by objective factors such as reputation and information For government officials and administrators, program quality is often measured through objective achievements including retention rates, graduation times, enrollment trends, starting salaries of graduates, graduate program enrollment percentages, and professional passing rates, aligning with the insights of Letcher and Neves.
Similarly, Astin (1993) defines student satisfaction as the students’ perceived value of their educational experiences at a university According to Muilenburg &
Research by Berge (2005) highlights significant differences in how students perceive their online learning experiences, which can impact their decisions to continue or discontinue courses Student perceptions play a crucial role in influencing their satisfaction with online education, as noted by Kenny (2003) Ultimately, student satisfaction in e-learning reflects their perceived value of their online experiences, emphasizing the importance of positive perceptions for successful online education outcomes (Carr, 2000).
Theoretical Background of student satisfaction model
In the online learning environment, student satisfaction is influenced by multiple key elements, including the instructor, technology, and interactivity, as identified by Bolliger and Martindale (2004) Additional factors such as communication with course constituents, course management issues, and the quality of course websites also play a significant role According to Liaw (2008), students’ perceptions of task value, self-efficacy, social ability, system quality, and multimedia instruction are crucial in shaping their satisfaction Furthermore, students need confidence in their ability to succeed in online learning, as highlighted by Bolliger & Wasilik (2009) Student satisfaction is closely linked to student performance and is a vital component in assessing faculty effectiveness.
Research from 2009 indicates a strong correlation between faculty satisfaction and student learning outcomes, highlighting the importance of maintaining motivated and content educators to enhance overall academic performance.
Researchers in psychology and information systems have identified key variables that significantly influence student satisfaction The models depicted in Figures 2.3.1, 2.3.2, 2.3.3, and 2.3.4 illustrate the critical factors affecting student satisfaction levels, providing valuable insights into the elements that enhance the overall educational experience.
Figure 2.3.1 Partial model of student satisfaction and retention (Oscar, Ali, &
According to Oscar, Ali, and Erdener (2005), their model highlights the vital connections between faculty, advising staff, and classes, emphasizing how these factors influence students’ university experiences and overall satisfaction This model helps researchers understand the impact of specific variables on the college experience in higher education institutions By examining these relationships, the model provides insight into how faculty interactions, advising quality, and coursework contribute to improved student satisfaction.
SATISFACTION tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Figure 2.3.2 A conceptual model of user’ satisfaction, behavioral intention, and effectiveness toward e-learning ( Liaw, 2008)
Liaw (2008) presents a conceptual model illustrating how learners' perceived satisfaction and perceived usefulness of e-learning are influenced by their individual characteristics The model also emphasizes that environmental factors play a significant role in shaping perceived satisfaction, perceived usefulness, and overall e-learning effectiveness Importantly, the study highlights that higher perceived satisfaction and usefulness positively impact learners’ behavioral intention to engage with e-learning platforms, underscoring the importance of these perceptions for successful e-learning outcomes.
Learners’ characteristics, such as self- efficacy, self- directedness, etc
Environmental factors, such as multimedia instruction, system quality, synchronous, and/or asynchronous interaction, etc
Behavioral intention of using e- learning
E-learning effectiveness tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Figure 2.3.3: Research model ( Eom, Ashill, and Wen, 2006)
Figure 2.3.3 highlights key factors influencing e-learning outcomes and student satisfaction in online education The study aims to identify essential elements that impact students' perceived learning success and satisfaction with e-learning systems The research model focuses on critical factors such as student self-motivation, learning styles, instructor expertise and facilitation, feedback quality, interaction levels, and course structure These elements collectively determine the effectiveness and perceived value of online education for students.
Course structure tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Figure 2.3.4 Dimensions and antecedents of perceived e-learner satisfaction (Sun et al., 2006)
According to Sun et al (2006), Figure 2.3.4 presents a comprehensive model highlighting the key factors influencing e-learning activities and learner satisfaction The model identifies six crucial dimensions—student, instructor, course, technology, design, and environment—that collectively encompass thirteen factors critical to effective e-learning experiences This framework underscores the importance of considering these six dimensions when evaluating the success of e-learning programs.
- Learner perceived interaction with others
This article examines key factors influencing e-learner satisfaction and graduation rates, highlighting the impact of computer anxiety and Internet self-efficacy on learner experiences It emphasizes the importance of instructor responsiveness, including timeliness and attitude toward e-learning, as critical components within the instructor dimension Additionally, the study explores course-related factors such as e-learning course flexibility and course quality, which significantly affect overall learner satisfaction and successful completion Ensuring effective instructor support and high-quality, flexible courses can enhance e-learning outcomes and student achievement.
The technology dimension encompasses technology quality and Internet quality, which are crucial for effective online learning environments In the design dimension, perceived usefulness and perceived ease of use play a significant role in user acceptance and engagement Additionally, the environmental dimension includes diversity in assessment methods and learners' perceived interaction with others, fostering a collaborative learning experience These key factors have been extensively discussed in previous research studies, highlighting their importance in educational technology integration.
Factors influencing the student satisfaction with e-learn
This study applies established theories and models to examine online student satisfaction at Vietnam universities, considering the research objectives, scope, and current national context The research identifies three key dimensions that influence satisfaction: instructor capability, learner attitude, and technology These factors are essential in understanding and enhancing the online learning experience in Vietnamese higher education institutions.
According to Marks (2000), instructor capability is a multidimensional concept, with researchers recognizing various perspectives on its nature The key differences among these perspectives lie in the number and components of instructor capability, highlighting its complex and diverse characteristics.
Braskamp and Ory (1994) identify six key components of instructor capability, including course organization and planning, clarity and communication skills, instructor-student interaction and rapport, course difficulty and workload, grading and examinations, and student self-learning Similarly, Marks (2000) highlights five essential components: course organization, course difficulty and workload, expectations and fairness of grading, instructor likability, and student-instructor interaction Ginns et al (2007), based on student perceptions of teaching quality, propose five components such as effective teaching, clear goals and standards, appropriate assessments, manageable workload, and generic skills development Incorporating these diverse perspectives underscores the multifaceted nature of effective instruction and the importance of various elements in enhancing teaching quality.
Research by Arbaugh (2000) indicates that increased interaction among learners leads to higher e-learning satisfaction In virtual learning environments, active communication between students and instructors enhances problem-solving and supports overall progress, contributing to a more effective and engaging online learning experience.
Piccoli et al (2001) said that interacting electronically could improve learning effects
Students with teachers, students with materials, and students with students are three kinds of interactions in e-learning activities (Moore, 1989) However, people cannot deny the fact that interactions between instructors and students play a certain role in e- learning (Webster & Hackley, 1997) Without conspicuous interactions between teachers and students, learners are more prone to distractions and difficulty concentrating on the course materials (Isaacs et al., 1995) E-learning requires better concentration than in traditional face-to-face interactions because it is capable of proceeding in almost everywhere ( Kydd& Ferry, 1994) For this study, the definition of learners’ interaction with others is learners’ perception of the level of interactions between students and instructors
Among the components of instructor capability mentioned above, it is obvious that there is a considerable overlap For that reason, this study mainly focuses on three components of instructor capability, including teaching capability, course organization, and instructor-student interaction The instructor’s knowledge and investment in the course, clarity, and communication skills belong to teaching capability Course organization refers to the structure of the course and instructor- tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg class attention, comprising asking questions, expressing ideas, and open discussions in the class According to Biggs (1999), it is impossible to deny the important role of instructor capability in teaching and learning simply because such capability will help students in catching the materials of the course Besides, instructor capability also assists students to understand the value and benefits of their learning Students highly evaluating their instructor capability will therefore be more interested in the course, leading to a higher level of satisfaction to their learning (Nguyen & Nguyen, 2010)
They will spend more time and make more effort in their study
H1a Teaching capability will have a positive impact on student satisfaction with e-learning in Vietnam
H1b Course organization will have a positive impact on student satisfaction with e-learning in Vietnam
H1c Instructor-student interaction will have a positive impact on student satisfaction with e-learning in Vietnam
It is impossible for researchers to deny the important role of learner attitude towards tehnology, such as computers or IT, in e-learning satisfaction (Sun et al.,
Learner attitude refers to students’ perceptions and impressions of participating in e-learning activities through computer usage Essentially, students rely on information technology as a crucial tool to facilitate their online learning experience In this context, instructors upload materials on the Internet, requiring learners to use various technologies and internet-connected devices A more positive attitude towards technology correlates with higher satisfaction and greater effectiveness in e-learning environments, enhancing overall learning outcomes (Sun et al., 2006).
Cole, 1983) There is no doubt that the positive attitude of students toward technology or computers lead to the increase in the chances of successful learning for themselves
Negative attitudes toward learning can diminish students' interest in studying, highlighting the importance of positive perceptions Learners' attitudes towards technology and computers play a crucial role in determining their overall learning satisfaction Hypothesis 2 aims to test the assumption that positive attitudes enhance learning experiences and satisfaction.
H2 Learner attitude towards technology will positively influence student satisfaction with e-learning in Vietnam
Research indicates that high-quality technology and reliable internet connectivity significantly enhance student satisfaction in e-learning environments (Sun et al., 2006) User-friendly software tools that are easy to navigate, requiring minimal effort, encourage learners to adopt and engage with digital learning platforms more willingly When technology systems are dependable and of high quality, students tend to experience more effective and satisfying learning outcomes (Piccoli et al., 2009).
Effective e-learning relies heavily on both technology and Internet quality, which are crucial for student engagement and success High-quality and reliable technology, including devices like microphones, earphones, and electronic blackboards, directly impact learners’ experiences Additionally, the perceived quality of network transmission speed and stability plays a vital role in ensuring smooth communication and access to learning materials Overall, both technology quality and Internet performance are key factors influencing the effectiveness of e-learning environments.
H3a Technology quality will positively influence student satisfaction with e- learning in Vietnam
H3b Internet quality will positively influence student satisfaction with e- learning in Vietnam
This study's research model identifies key factors influencing student satisfaction with e-learning in Vietnam, based on established conceptual frameworks by researchers such as Oscar, Ali, & Erdener (2005), Sun et al (2006), Liaw (2008), and Eom, Ashill, and Wen (2006) The model evaluates these factors across three dimensions: instructor capability, learner attitude toward computers, and technology Within the instructor dimension, factors include course organization, teaching capability, and student-instructor interaction, while learner attitude focuses solely on the attitude toward technology The technology dimension encompasses the quality of technology and Internet connectivity, collectively forming a comprehensive framework illustrated in Figure 2.5.1.
Figure 2.5.1: Factors of e-learner satisfaction model
Student-instructor interaction tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Methodology
Research process
Defining the research problem was the essential first step in this study, establishing a clear focus for the investigation A comprehensive review of relevant literature followed, identifying key concepts related to student satisfaction with e-learning These concepts formed both the theoretical foundation and the basis for developing the research model and hypotheses, ensuring a targeted and effective approach to understanding e-learning experiences.
The research design was developed to identify data sources, data collection methods, and measurement scales adapted from previous studies, ensuring validity and relevance A draft questionnaire was created based on these measurement scales and translated into Vietnamese to facilitate accurate responses The questionnaire was then reviewed and revised by the researcher’s supervisor to correct errors and ensure clarity before the data collection phase Additionally, the study incorporated a specific sampling strategy and employed appropriate data analysis methods to ensure reliable and meaningful results.
This research was conducted in two main stages: preliminary and official The preliminary stage involved qualitative research through a focus group to refine measurement scales and a quantitative pilot survey to test their reliability In the official stage, a finalized questionnaire was employed in a comprehensive quantitative survey to collect data for analysis, with results used to complete the study The entire process is illustrated in Figure 3.3.1, providing a clear overview of the research methodology.
Data needs and data resources
Delete low item- total correlation items (< 0.3)
Multiple Regression Analysis tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Preliminarily qualitative research
To ensure accurate responses from students, it is essential for all participants to thoroughly understand the questionnaire Therefore, researchers developed a bilingual questionnaire in both English and Vietnamese (see Appendix A) The study, which included a pilot test and a main survey, was conducted in Ho Chi Minh City, a major hub for business and education in Vietnam.
This study emphasizes the importance of validating measurement tools to ensure their appropriateness within the research context, focusing on how instructor capability, learner attitudes towards computers, and technology influence student satisfaction To achieve this, a pilot study was conducted, comprising both qualitative and quantitative methods The qualitative phase involved in-depth interviews with six master's students at the University of Economics Ho Chi Minh City (UEH) to assess the relevance and clarity of the measurement items The primary goal of this pilot research was to verify that the measures accurately captured the intended constructs, laying a solid foundation for the subsequent full-scale study.
In addition, the pilot quantitative survey with participation of sixty learners was undertaken at universities in Ho Chi Minh city.
Sampling design
Determining the appropriate sample size depends on factors such as desired reliability and data analysis methods According to Hair, Anderson, Tatham, and Black (1998), a minimum sample size of 100 to 150 elements is recommended for robust research results.
Bollen (1989) suggested that a minimum sample size of five elements per estimated parameter is necessary Harris (1985) proposed a formula for regression analysis, indicating that the sample size should be at least n >= 104 + m, where m represents the number of independent variables For Exploratory Factor Analysis, it is recommended to have a sample size of at least 50 elements, with 100 being preferable, and each parameter requiring a minimum of five elements In this study, data was collected from approximately 300 university students using electronic tools, emails, and paper-based surveys, serving as the primary data source Additionally, secondary data was gathered from related articles, business journals, internet sources, and websites to enrich the research. -🌸 **Ad** 🌸 Streamline your academic research and data analysis with [Claude’s](https://pollinations.ai/redirect/claude) advanced AI tools designed for content creators and SEO experts!
Measurement
Based on the reviewed literature, the study examines four key constructs: instructor capability, learner attitude, technology dimension, and learner satisfaction, comprising a total of 27 measurement items These items are evaluated using a five-point Likert scale, with responses ranging from 1 (strongly disagree) to 5 (strongly agree) To ensure rigorous statistical analysis, the research employs the Statistical Package for the Social Sciences (SPSS).
Quantitative Research
This research begins by assessing the current situation of e-learning challenges in Ho Chi Minh City, Vietnam, to identify specific problems The primary purpose is then defined to address these issues A comprehensive literature review follows, focusing on factors influencing student satisfaction in e-learning and establishing a theoretical foundation using relevant models Finally, the research framework is designed, incorporating both mail and paper survey approaches to gather comprehensive data.
The development of questionnaires for both mail and paper surveys will be based on comprehensive literature reviews and expert consultations to ensure their validity and reliability Quantitative data will then be collected through self-administered questionnaires distributed via mail and in person, facilitating accurate and reliable data collection for the study.
Finally, conclusions and suggestions were made marking the end of the research process
The research commenced in August 2013, beginning with identifying the research dilemma and objectives Developed questionnaires were reviewed, refined through pilot testing and expert consultations, then distributed to respondents in Ho Chi Minh City in September 2013, with data collected over two months Data analysis was conducted in December 2013, and the results were interpreted in February 2014 The study was finalized with report writing completed by the end of March 2014.
Data analysis
The data analysis process began with descriptive statistics to summarize respondent demographics, followed by reliability testing using Cronbach’s Alpha to ensure consistency of measurement Exploratory Factor Analysis (EFA) was conducted to identify the underlying relationships between items and constructs, while subsequent tests for multicollinearity ensured the validity of the analysis Regression analysis was then performed to examine the relationships between predictors and dependent variables, providing insights into the factors influencing the outcomes Hypothesis testing was carried out to determine whether the proposed hypotheses were supported, allowing for informed conclusions about the research questions.
Research Results
Data statistical analysis
By performing surveys via electronic tools or mails, and papers, there were total 600 questionnaires collected from students at universities in Ho Chi Minh City
In fact, 301 questionnaires were uncompleted or sloppy done Most of respondents have experienced e-learning The usable number obtained to put into analysis was 299 observations, including 75 questionnaires via e-mails and 224 ones via papers
According to the gender table of appendix C, in the 299 respondents, there were 167 females, equivalent to 55.9% In comparison with females, males were lower, accounting for 44.1%
Most respondents, accounting for 69.9% (209 individuals), were aged between 22 and 35 years, as shown in the age table (see Appendix C) The 36-45 age group comprised 30.1% (90 people), while there were no respondents over 45 years old.
Based on the data presented in Table 3 of Appendix C, the majority of respondents held bachelor's and master's degrees, accounting for 42.1% (126 individuals) and 57.9%, respectively.
The descriptive statistics for the questionnaire variables are presented in Table 4.1.1 and Table 4.2 (see Appendix C) The framework consisted of 27 items, including 12 items assessing instructor capability, 5 items on learner attitude, 6 items related to the technology dimension, and 4 items measuring learner satisfaction As shown in Table 4.1.1, the mean scores indicate a generally positive perception, with most items scoring above 3.00 on a five-point Likert scale, except for the learner attitude construct The standard deviations were mostly within acceptable ranges, suggesting consistent responses with minimal variation among participants.
Cronbach’s Alpha coefficient of reliability test
The Cronbach’s Alpha coefficient of reliability test was applied for each scale in this research model According to Nguyen (2012), Nancy, Karen, and George
(2005), Hoang, and Chu (2008), Cronbach’s Alpha reliability coefficient belongs from 0.7 to 0.8 is acceptable in the research
In the Item-Total Statistics table, the Corrected Item-Total Correlation is a crucial metric to evaluate item quality According to Nancy et al (2005), a correlation of 40 or above indicates that the item is well-related to other items and is suitable for inclusion in a summated rating scale Conversely, items with lower or negative correlations (less than 30) may not fit well within the scale, suggesting the need to assess potential wording issues or conceptual discrepancies to ensure accurate measurement.
You may want to modify or delete such items” (p 67)
The results of Cronbach’s Alpha coefficient and Corrected Item-Total Correlation for each scale are summarized in tables 4.2.1 to 4.2.8 Cronbach’s Alpha for the Instructor Capability (IC) scale was 0.893, indicating good internal consistency within the acceptable range of 0.7 to 0.8 Additionally, most items in the analysis demonstrated acceptable item-total correlations, with values exceeding 0.3, supporting the reliability of the scales used.
Table 4.2.1: Reliability Statistics tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Cronbach's Alpha Based on Standardized Items N of Items
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
The Cronbach’s Alpha for learners' attitude toward computers (LA) was 0.846, indicating an acceptable level of internal consistency as it exceeds the 0.7 threshold Additionally, the item-total correlations were all above 0.3, demonstrating that each item contributed meaningfully to the overall scale and ensuring the reliability of the measurement.
Cronbach's Alpha Based on Standardized Items N of Items
846 846 5 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Cronbach’s Alpha of technology (TE) in the table 4.2.5 was satisfied because its value (equals 782) was above 0.7 In table 4.2.6, apart from Technology Quality
Most item-total correlations in the scale were above 0.3, indicating acceptable consistency, except for Technology Quality 01, which had a correlation of 0.284 This low correlation suggests that Technology Quality 01 does not adequately contribute to the overall scale and should be considered for removal to improve the scale's reliability.
Cronbach's Alpha Based on Standardized Items N of Items
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Internet Quality 01 17.71 15.692 573 409 739 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
The Cronbach’s Alpha for learner satisfaction (LS) was a reliable 0.829, indicating excellent internal consistency, as it exceeds the acceptable threshold of 0.7 Additionally, the item-total correlations in Table 4.2.8 were adequate, with all values surpassing 0.3, demonstrating that each item effectively contributed to measuring learner satisfaction.
Cronbach's Alpha Based on Standardized Items N of Items
Scale Mean if Item Deleted
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Exploratory Factor Analysis ( EFA) result
According to Nguyen (2012), Nancy et al (2005), Hoang, and Chu (2008), a KMO value greater than 0.7 indicates adequate sampling adequacy for factor analysis, while a value below 0.5 suggests that the data are unsuitable for such analysis Ensuring a high KMO measure is essential for reliable and valid results in statistical research.
(2012) provided the following rules of KMO, including: excellent (KMO>= 0.9), good (KMO>= 0.8), acceptable (KMO>= 0.7), questionable (KMO>= 0.6), poor (KMO>= 0.5), and unacceptable (KMO< 0.5)
The Bartlett test indicates that the variables are sufficiently correlated to justify conducting factor analysis, as evidenced by its significant result with a p-value less than 0.05 (Nancy et al., 2005) This emphasizes the importance of the Bartlett test in validating the suitability of data for factor analysis in research studies.
When analyzing factor analysis results, it is essential to review the Total Variance Explained table, as it shows how variance is distributed among potential factors (Nancy et al., 2005) Eigenvalues indicate the amount of explained variance, with values greater than 1.0 being particularly significant According to Nancy et al (2005), "When the eigenvalue is less than 1.0, this means that the factor explains less information than a single item would have explained" (p 82), suggesting that such factors are typically not of interest in the analysis.
When analyzing factor loadings in the Rotated Factor Matrix, it is essential to consider the threshold values; Nguyen (2012) indicates that a meaningful factor loading is typically above or equal to 0.707, while practical research often accepts loadings of 0.50 or higher Items with the highest factor loadings are associated with their respective factors, ensuring construct validity Items that do not meet these criteria should be omitted to maintain the integrity of the construct.
Table 4.3.1: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy .882 Bartlett's Test of Sphericity Approx Chi-Square 4.284E3 df 351
Based on Table 4.3.1, the KMO value was 0.882, which exceeds the acceptable threshold of 0.7, indicating that the data is suitable for factor analysis Additionally, the Sig value was 0.000, significantly less than the 0.05 threshold, confirming the statistical significance of the results Therefore, the analysis method applied is valid and appropriate for the data.
Loadings Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.
This study applied Exploratory Factor Analysis (EFA) using Principal Axis Factoring and Promax rotation to analyze questionnaire data designed to measure four dimensions: instructor capability, learner attitude, and technology Six factors were extracted, each with eigenvalues greater than 1 (9.082, 3.132, 1.830, 1.464, 1.133, and 1.037), collectively explaining 65.476% of the variance, exceeding the 50% threshold The six factors accounted for specific variance contributions: 33.638%, 45.240%, 52.016%, 57.438%, 61.633%, and 65.476%, respectively, with all 22 items loading above 0.5, confirming construct validity The EFA results indicate that learner satisfaction is influenced by three independent variables, with no changes in the original constructs The scree plot demonstrates a clear decline in eigenvalues after the first four components, supporting the suitability of the factor solution.
Independence of residual
The Durbin – Waston test is a number that tests for autocorrelation in the residuals from a statistical regression analysis (Nancy et al., 2005) The value between
The Durbin-Watson statistic ranges between 0 and 4, with a value above 2 indicating negative autocorrelation and a value below 2 suggesting positive autocorrelation In Table 4.1.1, the Durbin-Watson value is 1.682, which is less than 2, indicating the presence of positive autocorrelation in the sample.
Std Error of the Estimate Durbin-Watson
1 740 a 548 538 2.46124 1.682 a Predictors: (Constant), IQ, LA, TC, IN, TQ, CO b Dependent Variable: LS
4.5 Test of normality of residual and homoscedasticity
Homoscedasticity, as explained by Berry and Feldman (1985) and Tabachnick and Fildell (1996), refers to the condition where the variance of errors is consistent across all levels of the independent variables In contrast, heteroscedasticity occurs when the variance of errors varies at different values of the independent variables, indicating uneven dispersion of errors in the data Recognizing and addressing these conditions is essential for accurate statistical analysis and reliable results.
The normality of residual and homoskedasticity were tested in the research
According to chart 1 and chart 2 in the Appendix B, the Regression Standardized Residual ( chart 1) and Normal P-P plot of Regression Standardized Residual ( chart
2) indicated that the residuals were normally distributed, the residual was relatively uncorrelated with the linear combination of predictors, and the variances of the residuals were constant Regression standardized predicted values in the chart 3 of Appendix B were distributed randomly The data therefore met the assumptions
4.6 Multicollinearity test tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Researchers needed to test for multicollinearity to ensure accurate analysis of predictor correlations The Variance Inflation Factor (VIF) values for the six independent variables—learner attitude towards technology (LA), teaching capability (TC), course organisation (CO), instructor-student interaction (IN), technology quality (TQ), and internet quality (IQ)—were all below 10, with values of 1.078, 2.191, 2.711, 1.782, 1.997, and 1.953 respectively This indicates that multicollinearity was not present among the predictor variables, ensuring the reliability of the study's results.
4.7 No significant outliers or influential points
Case Number Std Residual LS Predicted Value Residual
B Std Error Beta Tolerance VIF
IQ 628 100 346 6.285 000 512 1.953 a Dependent Variable: LS tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Casewise Diagnostics helps the studiers find the unvalued data in the analysis
The data is not outlier when its Standardized Residual belongs the range of between -
3 and +3 (Nancy et al., 2005) According to table 4.7.1, the Standardized Residual of the case number 299 was 3.386, above +3 Therefore, this means that the case number
299 was outlier, and the researcher could consider to delete or to review it
Model Sum of Squares df Mean Square F Sig
Total 3910.970 298 a Predictors: (Constant), IQ, LA, TC, IN, TQ, CO b Dependent Variable: LS
The model summary table (Table 4.4.1) indicates that the multiple correlation coefficient (R) is 54.8%, with an R-squared value of 548, demonstrating that approximately 54.8% of the variability in learning success (LS) can be explained by the combined predictors The adjusted R-squared value of 538 further confirms that about 53.8% of the variance in LS is predicted by the integrated effects of IQ, leadership ability (LA), technology (TC), innovation (IN), teaching quality (TQ), and communication (CO).
The Anova table (table 4.8.1) indicated that F= 58.936 and was significant
The combination of predictors significantly forecasted student satisfaction (LS) in e-learning, as evidenced by a p-value (Sig.) below 0.05 in Table 4.8.1 This indicates that the regression model is statistically significant and effectively predicts student satisfaction outcomes, highlighting its importance for understanding e-learning success.
Effective teaching capability significantly enhances student satisfaction with e-learning in Vietnam, leading to improved learning experiences and engagement Strong instructional skills and clear communication by educators are essential for fostering a positive online learning environment Improving teaching methodologies can directly impact student outcomes and increase overall satisfaction with e-learning platforms.
According to the table 4.6.1, it is clear that teaching capability (TC) (with ò.104, t= 1.784, sig = 075 > 5%) had a positive effect on learner satisfaction in e- learning In other words, H1a was unsupported
H1b Course organization will have a positive impact on student satisfaction with e-learning in Vietnam
In the table 4.6.1, course organization (CO); including ò= 154, t= 2.382, and sig = 018 < 5%; had a positive effect on learner satisfaction in e-learning Hence, H1b was supported
H1c Instructor-student interaction will have a positive impact on student satisfaction with e-learning in Vietnam
Obviously, instructor-student interaction (IN) (with ò= 098, t= 1.865, sig .063 > 5%) had a positive effect on learner satisfaction in e-learning in the table 4.6.1
H2 Learner attitude towards technology will positively influence student satisfaction with e-learning in Vietnam
The study found that learner attitude towards technology (LA) showed no significant correlation with learning strategies (LS), as indicated by a correlation value of 0.033, t-value of 0.798, and a p-value of 0.425, which exceeds the 5% significance threshold Consequently, the results do not support the second hypothesis, suggesting that learner attitude towards technology does not have a positive influence on learning strategies.
H3a Technology quality will positively influence student satisfaction with e- learning in Vietnam
H3b Internet quality will positively influence student satisfaction with e- learning in Vietnam
Based on the analysis, H3a and H3b were ultimately supported The coefficients table indicates that both technology quality (TQ) and internet quality (IQ) have significant p-values, highlighting their positive influence on user experience and overall system performance These findings suggest that improvements in technology and internet quality are crucial for enhancing user satisfaction and optimizing system functionality.
The study found a statistically significant relationship between Technical Efficiency (including TQ and IQ) and Life Satisfaction (LS) Specifically, the beta values for TQ and IQ were 202 and 346, indicating that Technical Efficiency positively influences Life Satisfaction These findings support Hypotheses 3a and 3b, confirming that higher levels of TQ and IQ are associated with increased LS.
Regression analysis reveals that both instructor capability (IC) and technology dimension (TE) significantly influence learner satisfaction (LS) in e-learning Specifically, factors such as course organization (CO), technology quality (TQ), and internet quality (IQ) within IC and TE play a vital role in enhancing LS in Vietnam In contrast, learner attitude (LA) has a comparatively minor impact on learner satisfaction, indicating that technical and instructional factors are primary drivers of positive e-learning experiences Optimizing instructor skills and technology infrastructure is essential for improving learner satisfaction in online education.
The analysis revealed that LS has the strongest positive correlation with IQ, with a beta weight of 0.346 and a p-value of 0.000, indicating a statistically significant relationship Additionally, LS is strongly positively related to TQ, demonstrated by a beta of 0.202 and a p-value of 0.000, confirming a significant association The relationship between LS and CO is also notably positive, with a beta of 0.154 and a p-value of 0.018, signifying statistical significance In contrast, LA shows no significant relationship with LS, with a beta of 0.033 and a p-value of 0.425, indicating the absence of a statistically meaningful connection.
Our analysis indicates that LA and IN did not demonstrate statistically significant positive relationships with LS, as their p-values exceeded the 5% significance level Conversely, variables such as TQ, IQ, and CO emerged as significant predictors of LS, highlighting their influential role These findings underscore the importance of TQ, IQ, and CO in understanding factors that impact LS, offering valuable insights for future research and practical applications.
Table 4.9.1: Results of the Testing Hypotheses
Research questions: The questions asked whether learner satisfaction impacted by instructor capability, learner attitutde and technology during the transaction on the internet
H1a Teaching capability will have a positive impact on student satisfaction with e-learning in Vietnam
H1b Course organization will have a positive impact on student satisfaction with e-learning in Vietnam
H1c Instructor-student interaction will have a positive impact on student satisfaction with e-learning in Vietnam
H2 Learner attitude towards computers will positively influence student satisfaction with e-learning in Vietnam
H3a Technology quality will positively influence student satisfaction with e- learning in Vietnam
H3b Internet quality will positively influence student satisfaction with e- learning in Vietnam
This research identified key factors influencing student satisfaction with e-learning at Vietnamese universities, highlighting instructor capability—encompassing teaching ability, course organization, and student-instructor interaction—and the technology dimension, including technology quality and internet connectivity The study also predicted the relative impact of each factor on e-learner satisfaction, with detailed results presented in Figure 4.9.2.
Figure 4.9.2 The final research model
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Multicollinearity test
tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
The researchers conducted a multicollinearity test to ensure accurate results regarding the relationships among predictor variables The Variance Inflation Factor (VIF) for the six independent variables—learner attitude towards technology (LA), teaching capability (TC), course organization (CO), instructor-student interaction (IN), technology quality (TQ), and internet quality (IQ)—were all below the critical threshold of 10, with values of 1.078, 2.191, 2.711, 1.782, 1.997, and 1.953, respectively These results indicate that multicollinearity was not present among the predictor variables Consequently, the absence of multicollinearity confirms the robustness of the regression analysis used in the study.
No significant outliers or influential points
Case Number Std Residual LS Predicted Value Residual
B Std Error Beta Tolerance VIF
IQ 628 100 346 6.285 000 512 1.953 a Dependent Variable: LS tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Casewise Diagnostics helps the studiers find the unvalued data in the analysis
The data is not outlier when its Standardized Residual belongs the range of between -
3 and +3 (Nancy et al., 2005) According to table 4.7.1, the Standardized Residual of the case number 299 was 3.386, above +3 Therefore, this means that the case number
299 was outlier, and the researcher could consider to delete or to review it.
Hypotheses Testing
Model Sum of Squares df Mean Square F Sig
Total 3910.970 298 a Predictors: (Constant), IQ, LA, TC, IN, TQ, CO b Dependent Variable: LS
The model summary indicates that the multiple correlation coefficient (R) is 54.8%, with an R-squared value of 0.548, demonstrating that approximately 54.8% of the variability in life satisfaction (LS) can be explained by the combined predictors—IQ, LA, TC, IN, TQ, and CO Additionally, the adjusted R-squared of 0.538 reflects that about 53.8% of the variance in LS is accounted for by these variables, highlighting their significant collective predictive power.
The Anova table (table 4.8.1) indicated that F= 58.936 and was significant
The analysis demonstrated that the combination of predictors significantly predicts student satisfaction in e-learning Specifically, Table 4.8.1 shows a p-value (Sig.) below 0.05, indicating the model's statistical significance Therefore, the study concludes that the model effectively explains factors influencing student satisfaction in online learning environments.
Effective teaching capabilities significantly enhance student satisfaction with e-learning in Vietnam High-quality instruction fosters better engagement, understanding, and learning outcomes, leading to a more positive online education experience Strengthening teachers' skills and methodologies is crucial for improving overall student satisfaction in the digital learning environment.
According to the table 4.6.1, it is clear that teaching capability (TC) (with ò.104, t= 1.784, sig = 075 > 5%) had a positive effect on learner satisfaction in e- learning In other words, H1a was unsupported
H1b Course organization will have a positive impact on student satisfaction with e-learning in Vietnam
In the table 4.6.1, course organization (CO); including ò= 154, t= 2.382, and sig = 018 < 5%; had a positive effect on learner satisfaction in e-learning Hence, H1b was supported
H1c Instructor-student interaction will have a positive impact on student satisfaction with e-learning in Vietnam
Obviously, instructor-student interaction (IN) (with ò= 098, t= 1.865, sig .063 > 5%) had a positive effect on learner satisfaction in e-learning in the table 4.6.1
H2 Learner attitude towards technology will positively influence student satisfaction with e-learning in Vietnam
The analysis indicates that learner attitude towards technology (LA) showed a low correlation with learning satisfaction (LS), with a value of 0.033, t-value of 0.798, and a significance level of 0.425 (p > 0.05) This suggests that there is no significant positive relationship between learner attitude and learning satisfaction, leading to the rejection of the second hypothesis.
H3a Technology quality will positively influence student satisfaction with e- learning in Vietnam
H3b Internet quality will positively influence student satisfaction with e- learning in Vietnam
In the study, both H3a and H3b hypotheses were evaluated, with technology quality (TQ) and internet quality (IQ) showing significant results The coefficients table indicated that the p-values for TQ and IQ confirmed their positive impact on the studied outcomes, highlighting the importance of high-quality technology and internet services for improving user experience and system performance.
The study found a statistically significant relationship between total entrepreneurial effort (TE), including task quality (TQ) and innovation quotient (IQ), and life satisfaction (LS) Specifically, beta values of 202 for TQ and 346 for IQ indicate that higher entrepreneurial effort positively influences life satisfaction These findings support hypotheses 3a and 3b, highlighting the beneficial impact of entrepreneurial activities on overall well-being.
Summary of the Results
Our regression analysis revealed that both instructor capability (IC) and technology dimension (TE) significantly influence learner satisfaction (LS) in e-learning, with key factors such as course organization (CO), technology quality (TQ), and internet quality (IQ) playing a vital role in Vietnam In contrast, learner attitude (LA) has a comparatively lesser impact on learner satisfaction, indicating that technological and instructional factors are primary drivers of positive e-learning experiences These findings highlight the importance of enhancing course structure and technology infrastructure to improve learner satisfaction in online education.
The beta weights indicated that LS had the strongest positive relationship with IQ, with an effect size of ò=0.346 and a statistically significant p-value of 0.000 Additionally, LS demonstrated a strong positive association with TQ, evidenced by ò=0.202 and p=0.000, confirming a significant correlation The relationship between LS and CO was also notably positive, with ò=0.154 and p=0.018, indicating a meaningful connection In contrast, LA showed no significant relationship with LS, as indicated by ò=0.033 and p=0.425, suggesting no statistically positive association.
In this study, LA, TC (including ò = 104 and p = 075 > 5%), and IN (ò = 098 and p = 063 > 5%) did not demonstrate statistically significant positive relationships with LS Conversely, typical predictors such as TQ, IQ, and CO showed significant correlations with LS, highlighting their crucial role in influencing the outcome For more recent thesis downloads and related academic resources, please visit our platform or contact us via email.
Table 4.9.1: Results of the Testing Hypotheses
Research questions: The questions asked whether learner satisfaction impacted by instructor capability, learner attitutde and technology during the transaction on the internet
H1a Teaching capability will have a positive impact on student satisfaction with e-learning in Vietnam
H1b Course organization will have a positive impact on student satisfaction with e-learning in Vietnam
H1c Instructor-student interaction will have a positive impact on student satisfaction with e-learning in Vietnam
H2 Learner attitude towards computers will positively influence student satisfaction with e-learning in Vietnam
H3a Technology quality will positively influence student satisfaction with e- learning in Vietnam
H3b Internet quality will positively influence student satisfaction with e- learning in Vietnam
Based on that result, this research re-identified the model of those factors contributing to student satisfaction with e-learning at Vietnam universities, there were instructor capability (including teaching capability, course organization, student- instructor interaction), and technology dimension (including technology quality and Internet quality) The strength of each factor on the e-learner satisfaction also is predicted, and presented in the below figure 4.9.2 tot nghiep down load thyj uyi pl aluan van full moi nhat z z vbhtj mk gmail.com Luan van retey thac si cdeg jg hg
Figure 4.9.2 The final research model
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