Introduction
Background
Education is essential for the development of a country, influencing its society, economy, and politics It is crucial for citizens to engage in lifelong learning, focusing on self-learning and the enhancement of professional skills and quality of life E-learning has emerged as a key method to meet these educational demands In Vietnam, the government prioritizes the establishment of a learning society, as highlighted in the eleventh Congress of the Communist Party of Vietnam (2011), which emphasizes the promotion of study encouragement, the development of a learning society, and the expansion of e-learning initiatives.
Vietnam's shift towards a market-oriented economy and its membership in the World Trade Organization (WTO) have created both opportunities and challenges for businesses This transition has opened new markets for exports, improved access to imported raw materials and technologies, and enhanced international business cooperation However, it has also intensified competition and introduced stricter standards for product quality and safety A significant challenge for Vietnam is the lack of knowledge in business management, which is compounded by the increasing demand for updated skills among the workforce To address this, Vietnamese universities and colleges are actively working to provide qualified business graduates to meet the needs of the labor market.
In Vietnam, higher education, as defined by the education laws of 2012, encompasses both undergraduate and postgraduate studies Postgraduate studies include master's and doctoral degrees, while undergraduate studies lead to diplomas and bachelor's degrees The higher education system in Vietnam comprises universities, research institutions, and colleges.
In Vietnam, universities are authorized to offer a range of programs, including college, undergraduate, master's, and doctoral degrees, as designated by the Prime Minister Research institutions can also provide doctorate programs, while colleges are limited to offering college degrees and lower-level undergraduate programs Over the past 11 years, the higher education system in Vietnam has undergone significant changes, including the establishment of new institutions and improvements in quality, with approximately 386 universities and colleges currently operating The advent of technology and the Internet has made it easier for individuals to access university courses through various online platforms, such as e-learning and online education, which have generated considerable interest in the education sector This shift towards online learning is seen as a transformative force that enhances educational delivery and competitiveness The rapid growth of the Internet, coupled with a rising demand for lifelong learning and budget constraints, has incentivized universities to incorporate e-learning into their curricula Institutions that fail to adopt modern technologies risk falling behind in the global educational landscape.
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)
The advancement of information and communication technology has made e-learning a prominent model in modern education E-learning offers significant benefits, such as facilitating interactions between learners and instructors, as well as among peers, regardless of time and location, through both asynchronous and synchronous learning networks These features of e-learning effectively meet the demands of contemporary society for flexible and accessible learning solutions.
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 the University of Information Technology, express dissatisfaction and disappointment with their e-learning experiences.
Research on student satisfaction is essential for determining if colleges and universities are meeting their educational missions A qualified graduate is the primary outcome of these institutions Satisfied students are more likely to put in greater effort compared to their unsatisfied peers (Bryant, 2006; Ozgungor, 2010) Consequently, satisfied students tend to engage more actively in their studies, which includes attending classes regularly and participating in their coursework.
Consequently, exploring the elements influencing the satisfaction of Vietnamese students is a need for academics and university administrators as well
This study aims to explore key factors influencing the satisfaction of Vietnamese students in e-learning environments Specifically, it analyzes the effects of various dimensions, including the instructor dimension, learner dimension, and technology dimension, on e-learner satisfaction.
Research plays a crucial role in education by helping universities understand the factors that influence e-learner satisfaction The findings provide insights on how institutions can enhance learner satisfaction and improve their e-learning initiatives Additionally, the study highlights the importance of integrating technological innovations in teaching and developing appropriate policies to enhance instructors' teaching capabilities.
This study seeks to identify the key factors influencing e-learners' satisfaction and to assess the impact of each factor on student satisfaction with e-learning The primary objectives of this research are to determine these critical factors and evaluate their relative strengths in contributing to overall e-learning satisfaction.
This article explores the key factors influencing student satisfaction in e-learning, including interaction quality, learners' attitudes towards technology, the quality of the technology used, and the reliability of internet connectivity.
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
This study focuses on e-learning students in Ho Chi Minh City, specifically analyzing data collected from local universities The research primarily targets post-graduate and graduated students, excluding other types of university students from consideration.
The paper is structured into five key sections: the introduction, which outlines the study; a literature review that presents the hypothesis; the methodology section detailing the research approach; the results section showcasing the findings; and finally, a discussion that covers implications, limitations, and the 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 presents the theoretical foundation of the research by defining key concepts such as instructor capability and learner attitude It explores their relationships within the literature, leading to the formulation of hypotheses for the study.
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
The paper is structured into five key sections: an introduction to the study, a literature review along with the hypothesis, a detailed methodology, the presentation of research results, and concluding with discussions, implications, limitations, 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 presents the theoretical foundation of the research by defining key concepts such as instructor capability and learner attitude It explores their relationships as discussed in existing literature, leading to the formulation of hypotheses for the study.
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 advancement in distance learning, where instructors and learners are separated by time and space (Raab, Ellis, & Abdon, 2002) Utilizing network technologies, e-learning facilitates learning anytime and anywhere, providing a web-based system that offers knowledge without time or geographic constraints (Sun et al., 2006) According to Burgess and Russell (2003), e-learning enables institutions to deliver consistent education, update content as needed, reduce travel costs, and offer on-demand training It leverages live streaming, electronic materials, and interactive discussions through message boards and chat rooms Researchers in psychology and information systems have identified key factors influencing e-learning success, including the technology acceptance model and the expectation-confirmation model (Ajzen; Bhattacherjee, 2001; Lin, Wu, & Tsai, 2005).
Many articles have discussed about the benefits of e-learning (Liaw, Huang, &
E-learning offers several advantages, including the flexibility to choose when to engage with online lessons and the independence from a lecturer's schedule (Bouhnik & Marcus, 2006) It allows students to express their thoughts and ask questions freely while accessing course materials at their convenience Capper (2001) highlights additional benefits such as the ability to learn anytime and anywhere, facilitating asynchronous interactions that keep discussions focused Group collaboration is enhanced through electronic messaging, fostering shared conversations and teamwork Furthermore, e-learning introduces new educational approaches, making innovative learning strategies more accessible and enabling learners to share their innovations with immediate support from electronic groups.
E-learning offers significant innovations over traditional learning methods by combining visual, audio, and interactive elements, making training more effective for diverse learners while reducing costs associated with printing, publishing, and distribution It allows students to control their learning pace, bypass unnecessary instructions, and still achieve course objectives Additionally, e-learning reduces expenses related to teacher salaries, classroom rentals, and travel, leading to substantial cost savings The advantages of e-learning include access to high-quality faculty worldwide, a reduction in learning time by 40-60%, consistent content delivery aligned with school requirements, and automated training program completion This flexibility enables students to learn anytime and anywhere, facilitating the completion of training programs alongside work commitments.
The rapid advancement of information technology and communications has led to the expansion of online training, offering learners opportunities for self-study and review Despite significant investments in the education system, the growing social and educational needs of the population remain unmet In response, the Party emphasizes the importance of promoting a learning movement that encompasses both formal and non-formal education, aiming for inclusive education and a lifelong learning society (Tam, 2013) Consequently, the Education and Training Sector has developed strategies to mobilize community resources, creating social learning opportunities for individuals of all ages and abilities, thereby enhancing knowledge and human quality (Tam, 2013) E-learning plays a crucial role in achieving these educational goals.
As Vietnam integrates into the global economy through its WTO membership, the education sector faces the challenge of equipping future citizens with the necessary skills and intelligence to thrive in a competitive environment E-learning has become a prevalent method worldwide, with nearly 90% of universities in Singapore and over 80% in the USA adopting this approach The rapid advancement of information technology in Vietnam has led to a significant increase in Internet users, transforming how they work, study, and entertain themselves Currently, e-learning programs in Vietnam are accessible through three primary channels: university courses, international programs, and courses developed by companies.
The Vietnam Ministry of Education and Training has made significant strides in integrating information technology into education, focusing on online learning for managers, teachers, and students The establishment of the e-learning website (el.edu.net) facilitates access to technology, while the adoption of open-source software Moodle enhances the management of online learning systems Additionally, the Ministry employs globally recognized SCORM technologies to advance cooperation in information technology To meet the country's needs, compliant formats of SCORM files, such as Exe, Lectora, and Voilet, are being published Furthermore, the Ministry has improved connectivity with a fiber optic cable providing 34 Mbps and 2 Mbps for international access, supported by Viettel's high-quality NET packages for educational institutions.
E-learning in Vietnam lags behind developed countries like Singapore and the USA, facing challenges such as low quality and quantity of resources, limited scope, and insufficient learner participation Additionally, the interaction rate between teachers and students online is low, with responses often lacking warmth and guidance There is also a notable deficiency in teaching methodologies and qualified staff at universities, where some institutions expand rapidly without ensuring training quality Consequently, the true value of university e-learning remains underappreciated, presenting significant obstacles in the e-learning process.
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 often explored in academic literature Numerous studies aim to clarify these ideas, develop metrics for their measurement, and analyze their interrelationships and effects on other factors.
Consumer satisfaction is defined as the positive evaluation of an individual's experiences related to purchasing and using a product (Hunt, 1977) Additionally, Tse and Wilton (1988) describe customer satisfaction as the response to the perceived difference between expected performance and actual performance after consumption.
In the context of education, student satisfaction is defined as the positive evaluations students make regarding their educational experiences (Oliver & DeSarbo, 1989) This satisfaction is influenced by their overall experiences, as noted by Oliver (1980), and is interconnected with various aspects of campus life, which collectively shape their satisfaction with the university (Seymour, 1993) According to Parasuraman et al (1985, 1988), satisfaction arises when the perceived performance of educational services meets or exceeds student expectations, while dissatisfaction occurs when there is a negative gap between performance and expectations.
Student satisfaction is a short-term attitude shaped by educational experiences, while perceived quality is influenced by objective information and reputation, independent of personal experience For government officials and administrators, program quality is often assessed through objective metrics such as retention rates, graduation timelines, enrollment statistics, average starting salaries, the percentage of graduates pursuing further education, and professional passing rates (Letcher & Neves).
Similarly, Astin (1993) defines student satisfaction as the students’ perceived value of their educational experiences at a university According to Muilenburg &
According to Berge (2005), students have varying perceptions of their online e-learning experiences, which can significantly impact their decisions to continue with a course (Carr, 2000) These perceptions also play a crucial role in determining their overall satisfaction with online learning (Kenny, 2003) In this research context, student satisfaction is defined as the perceived value students derive from their online e-learning experiences.
Theoretical Background of student satisfaction model
In the online learning environment, several elements significantly impact student satisfaction Key factors identified by Bolliger and Martindale (2004) include the instructor, technology, and interactivity, along with communication among course participants, course management issues, and the effectiveness of course management systems Liaw (2008) emphasizes the importance of students' perceptions of task value, self-efficacy, social ability, system quality, and multimedia instruction Confidence in their ability to succeed in online learning is crucial for students (Bolliger & Wasilik, 2009) Furthermore, student satisfaction is closely linked to performance and plays a vital role in assessing faculty satisfaction.
In 2009, a significant correlation was identified between faculty satisfaction and student learning outcomes This relationship highlights the importance of faculty contentment in enhancing the educational experience for students.
Numerous researchers in the fields of psychology and information systems have identified key variables that influence student satisfaction The models illustrated in figures 2.3.1, 2.3.2, 2.3.3, and 2.3.4 depict the various factors that impact this important aspect of the educational experience.
Figure 2.3.1 Partial model of student satisfaction and retention (Oscar, Ali, &
The model presented by Oscar, Ali, & Erdener (2005) emphasizes the connections between faculty, advising staff, and classes, allowing researchers to assess how these essential elements influence students' experiences at universities and, consequently, their satisfaction This approach aims to analyze a specific set of variables that contribute to understanding the college experience in higher education institutions and its effect on 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 that explores the relationships between learner satisfaction, behavioral intention, and e-learning effectiveness The model illustrates that learners' characteristics significantly influence their perceived satisfaction and perceived usefulness of e-learning products Additionally, environmental factors also play a role in shaping these perceptions Importantly, the model indicates that higher levels of perceived satisfaction and perceived usefulness positively affect learners' intentions to engage with e-learning.
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 the factors influencing e-learning outcomes and student satisfaction The researchers aim to explore key elements that shape students' perceived learning outcomes and satisfaction in university online education through e-learning systems The model identifies several critical factors, including student self-motivation, learning styles, instructor knowledge and facilitation, feedback, interaction, and course structure.
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 summarizes the key factors influencing e-learning activities and learner satisfaction The assessment encompasses six dimensions: student, instructor, course, technology, design, and environment.
- Learner perceived interaction with others
E-learner satisfaction is influenced by various factors, including computer anxiety and learner Internet self-efficacy Key elements in the instructor dimension are the timeliness of instructor responses and their attitude towards e-learning Additionally, course flexibility and quality are crucial components in the course dimension that affect overall learner satisfaction.
The technology dimension encompasses both technology quality and Internet quality In the design dimension, key factors include perceived usefulness and perceived ease of use Additionally, the environmental dimension highlights the importance of diversity in assessment and learners' perceived interaction with others These elements have been explored in various studies by previous researchers.
Factors influencing the student satisfaction with e-learn
This study aims to apply established theories and models to assess online student satisfaction at universities in Vietnam, focusing on three key dimensions: instructor capability, learner attitude, and technology.
Research by Marks (2000) indicates a consensus among scholars that instructor capability is a multidimensional concept, despite varying perspectives on its definition The distinctions arise from the number and nature of its components.
Braskamp and Ory (1994) identify six key components of instructor capability: 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) outlines five components, which include course organization, course difficulty and workload, expectations and fairness of grading, instructor likability, and student-instructor interaction Additionally, Ginns et al (2007) propose five components based on student perceptions of teaching quality, encompassing good teaching, clear goals and standards, appropriate assessment, appropriate workload, and the development of generic skills.
Increased interaction among learners significantly enhances e-learning satisfaction, as noted by Arbaugh (2000) In virtual learning environments, effective communication between learners and instructors is crucial for addressing challenges and fostering academic progress.
Piccoli et al (2001) said that interacting electronically could improve learning effects
E-learning activities involve three types of interactions: students with teachers, students with materials, and students with each other (Moore, 1989) However, the significance of instructor-student interactions cannot be overlooked, as they play a crucial role in the e-learning experience (Webster & Hackley, 1997) A lack of meaningful interactions between teachers and students can lead to increased distractions and difficulties in focusing on course content (Isaacs et al., 1995) E-learning demands greater concentration compared to traditional face-to-face settings, as it can occur in virtually any environment (Kydd & Ferry, 1994) In this study, learners' interaction is defined as their perception of the level of engagement between students and instructors.
This study emphasizes three key components of instructor capability: teaching capability, course organization, and instructor-student interaction Teaching capability encompasses the instructor's knowledge, investment in the course, clarity, and communication skills Course organization pertains to the course structure and the ability to engage students through questions, idea expression, and open discussions According to Biggs (1999), instructor capability is crucial in teaching and learning, as it aids students in grasping course materials and recognizing the value of their education When students perceive high instructor capability, their interest in the course increases, resulting in greater satisfaction with their learning experience (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 of participating in e-learning activities through computer usage Specifically, students rely on information technology as essential tools for e-learning, accessing materials published by instructors online A positive attitude towards technology significantly enhances learners' satisfaction and effectiveness in an e-learning environment.
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 can diminish learners' interest in studying, highlighting the importance of their perceptions of technology and computers in relation to learning satisfaction This leads to the testing of Hypothesis 2.
H2 Learner attitude towards technology will positively influence student satisfaction with e-learning in Vietnam
Research indicates that the quality of technology and the Internet significantly impact student satisfaction in e-learning (Sun et al., 2006) User-friendly software tools that require minimal effort, such as simple ideas and intuitive interfaces, enhance the learning experience Consequently, when learners encounter fewer barriers, their satisfaction increases (Sun et al., 2006) Thus, higher quality and reliability in information technology lead to more effective student learning outcomes (Piccoli et al.).
In e-learning, the quality of technology and Internet connectivity significantly impacts student learning outcomes Video conferencing tools can enhance discussions and study sessions (Isaacs et al., 1995) Research indicates that the reliability of technology and the speed of network transmission are crucial factors affecting learners' experiences (Webster & Hackley, 1997) Technology quality refers to learners' perceptions of the IT tools used in e-learning, including microphones, earphones, and electronic blackboards Similarly, Internet quality is defined by the network performance as perceived by the learners.
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 presents a research model that identifies key factors influencing student satisfaction with e-learning in Vietnam, based on the conceptual frameworks of various researchers The model evaluates three main dimensions: instructor capability, learner attitude toward technology, and technology quality Within the instructor dimension, critical factors include course organization, teaching capability, and student-instructor interaction The learner dimension focuses on the learner's attitude toward technology, while the technology dimension encompasses technology quality and Internet quality The framework developed in this study is 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
The first priority in the study was to define the research problem, establishing it as the initial step Following this, a literature review was conducted to identify relevant concepts related to student satisfaction with e-learning These concepts were essential in forming the foundation for the proposed research model and hypotheses in the subsequent phase.
The research design focused on identifying data resources, data collection methods, measurement scales, sampling design, and data analysis techniques A draft questionnaire was created based on measurement scales from prior studies and translated into Vietnamese The researcher’s supervisor then reviewed the draft to correct any errors before the research commenced.
The research was conducted in two phases: a preliminary phase and an official phase The preliminary phase involved qualitative research through a focus group to refine measurement scales, alongside a quantitative pilot survey to evaluate the reliability of these scales In the official phase, the finalized questionnaire was utilized in a quantitative main survey to gather data for analysis, which was essential for completing the study The entire research process is illustrated in Figure 3.3.1.
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 them to fully comprehend the questionnaire Consequently, researchers developed the questionnaire in both English and Vietnamese (refer to Appendix A) The study, which included a pilot study and a main survey, was conducted in Ho Chi Minh City, a key hub for business and education in Vietnam.
To ensure the appropriateness of the constructs for this study, we will examine the impact of instructor capability, learner attitude towards computers, and technology dimension on student satisfaction A pilot study will be conducted, consisting of both qualitative and quantitative components The qualitative aspect will involve in-depth interviews with six students enrolled in the master's program at the University of Economics Ho Chi Minh City (UEH) to validate the content of the measures.
In addition, the pilot quantitative survey with participation of sixty learners was undertaken at universities in Ho Chi Minh city.
Sampling design
The required sample size is influenced by various factors, including the desired level of reliability and the chosen data analysis methods Hair, Anderson, Tatham, and Black (1998) suggest that a minimum sample size should range from 100 to 150 elements.
Bollen (1989) recommended a minimum sample size of five elements for each estimated parameter, while Harris (1985) proposed a formula for regression analysis indicating that the sample size (n) should be at least \( n \geq 104 + m \), where \( m \) represents the number of independent variables For Exploratory Factor Analysis, a sample size of 50 is acceptable, with 100 being preferable, and each parameter requiring a minimum of five elements In this research, a survey was conducted with approximately 300 university students using electronic tools, emails, and paper-and-pencil methods The data collected from the questionnaire served as the primary data, supplemented by secondary data from related articles, business journals, and online resources.
Measurement
The study identifies 27 key items related to instructor capability, learner attitude, technology dimension, and learner satisfaction, which will be assessed using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree) Additionally, the research will employ the Statistical Package for the Social Sciences (SPSS) for statistical analysis.
Quantitative Research
The research commenced by assessing the current state of e-learning challenges in Ho Chi Minh City, Vietnam Following this, the research purpose was clearly defined A literature review was conducted, emphasizing the factors influencing student satisfaction in e-learning, which provided a theoretical foundation for the study Subsequently, the research framework was developed, incorporating both mail and paper survey methodologies.
The questionnaires for the mail and paper surveys will be created based on literature reviews and expert consultations, focusing on the validity and reliability of the measurements Subsequently, quantitative data will be gathered using self-administered questionnaires from both the mail and paper surveys.
Finally, conclusions and suggestions were made marking the end of the research process
The research commenced in August 2013, focusing on identifying the research dilemma and objectives Questionnaires were developed, reviewed, and refined through a pilot test and expert consultancy, then distributed to respondents in Ho Chi Minh City in September 2013, with data collection concluding two months later Data analysis took place in December 2013, and the results were interpreted in February 2014 The research process concluded by the end of March 2014 with the completion of the final reports.
Data analysis
The data analysis process involved several key steps Initially, a descriptive analysis was conducted to gather demographic information about the respondents Following this, Cronbach’s Alpha coefficients were utilized to assess reliability Exploratory Factor Analysis (EFA) was then performed to identify relationships between various items and constructs After EFA, a test for multicollinearity was necessary to ensure the validity of the research Regression analyses were subsequently carried out to examine the relationships between the predictors and the dependent variables Hypothesis testing was also an essential component, allowing the researchers to determine the support for their proposed hypotheses Ultimately, the implications of the results were discussed in the context of the study.
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
A total of 301 questionnaires were either incomplete or poorly completed, but 299 valid responses were used for analysis Among these, 75 were collected through email and 224 were submitted on paper, with most respondents having prior experience with e-learning.
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%
The majority of respondents, 69.9% (209 individuals), were aged between 22 and 35 years, as shown in the age table (see appendix C) In contrast, those aged 36 to 45 years comprised a smaller portion at 30.1% (90 individuals), with no respondents falling into the over 45 age category.
According to the data presented in Table 3 of Appendix C, the majority of respondents held bachelor's and master's degrees, comprising 42.1% (126 individuals) and 57.9%, respectively.
The descriptive statistics evaluating the questionnaire variables of the respondents are presented in Table 4.1.1 and Table 4 (see Appendix C) The framework consists of 27 items, including 12 related to instructor capability, 5 to learner attitude, 6 to technology dimension, and 4 to learner satisfaction As indicated in Table 4-1-1, the mean scores for most research items were above 3.00 on a five-point Likert scale, highlighting a positive perception, with the exception of learner attitude Additionally, the standard deviation values were generally acceptable, suggesting minimal variation among the results.
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 crucial for evaluating item quality According to Nancy et al (2005), a correlation of 40 or higher indicates that the item is well correlated with other items, making it a valuable component of the summated rating scale Conversely, items with lower correlations may not fit the scale effectively, and those with negative or low correlations (below 30) should be scrutinized for potential wording issues and conceptual alignment.
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 detailed in tables 4.2.1 to 4.2.8 In table 4.2.1, the Cronbach’s Alpha for instructor capability (IC) was deemed acceptable, with a value of 893, falling within the range of 0.7 to 0.8 Additionally, table 4.2.2 indicates that most items exhibited acceptable item-total correlations, as they were above 0.3.
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 learner attitude toward computers (LA) was found to be 846, indicating an acceptable level of reliability as it exceeds the threshold of 0.7 Additionally, the item-total correlations presented in table 4.2.4 were significant, with values greater than 0.3, further supporting the validity of the items.
Cronbach's Alpha is a measure of internal consistency based on standardized items It is commonly used to assess the reliability of a set of items in a survey or test For the latest updates and resources, you can download the full thesis by contacting us at vbhtj mk gmail.com.
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 were above 0.3, indicating their acceptance; however, the item-total correlation for Technology Quality 01 was 0.284, which is below the acceptable threshold This suggests that this item is not suitable and may be removed from the scale.
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
Technology Quality 04 17.73 15.265 573 405 738 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) in Table 4.2.7 was a strong value of 829, indicating reliability as it exceeds the threshold of 0.7 Additionally, Table 4.2.8 shows that the item-total correlations for the items were satisfactory, with all values surpassing 0.3.
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
The Kaiser-Meyer-Olkin (KMO) measure is considered valuable in statistical analysis, with a recommended threshold of greater than 0.7 for adequacy, as noted by Nguyen (2012), Nancy et al (2005), and Hoang and Chu (2008) A KMO value below 0.5 indicates that the measure is inadequate.
(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 a strong correlation among the variables, highlighting its significance in the research The analysis reveals a significant (Sig) value of the Bartlett test that is less than 0.05, as noted by Nancy et al (2005).
In research analysis, the Total Variance Explained table is crucial as it details how variance is allocated among various factors (Nancy et al., 2005) The eigenvalue, which indicates the amount of explained variance, should be highlighted in this table, particularly when it exceeds 1.0 According to Nancy et al (2005), an eigenvalue below 1.0 suggests that the factor provides less information than a single item, making it unworthy of consideration in the analysis (p.82).
When analyzing data, it is essential to examine the factor loadings in the Rotated Factor Matrix table According to Nguyen (2012), a factor loading value of 0.707 or higher is considered useful In practical research, a factor loading of 0.50 or greater is deemed acceptable Items with the highest factor loadings are associated with their respective factors, while those that do not meet the required threshold should be excluded from 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
The KMO value of 0.882, which exceeds the acceptable threshold of 0.7, indicates that the analysis is valid Additionally, the significance value of 0.000 further supports the reliability of the results.
Loadings Rotation Sums of Squared Loadings
The extraction method utilized in this study was Principal Component Analysis, which identified six factors related to instructor capability, learner attitude, and technology dimensions The Eigenvalues for these factors were 9.082, 3.132, 1.830, 1.464, 1.133, and 1.037, all exceeding one The extraction sums indicated that these factors accounted for 33.638%, 45.240%, 52.016%, 57.438%, 61.633%, and 65.476% of the variance, respectively Notably, twenty-two items had loadings greater than 0.5, and the Total Variance Extracted explained 65.476% of the variance, confirming the model's adequacy The results of the Exploratory Factor Analysis (EFA) demonstrated that learner satisfaction was influenced by three independent variables, with no changes in the constructs' items The research achieved significant values, indicating convergent validity, and the scree plot illustrated a decline in eigenvalues after the first four components, reinforcing the model's suitability.
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 from 0 to 4, with values above 2 indicating negative autocorrelation and values below 2 indicating positive autocorrelation In Table 4.1.1, the Durbin-Watson value is 1.682, which is less than 2.0, signifying 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 defined by Berry and Feldman (1985) and Tabachnick and Fidell (1996), refers to the condition where the variance of errors remains 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.
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 conducted a multicollinearity test to ensure accurate correlations among predictor variables The Variance Inflation Factor (VIF) for independent variables is typically considered problematic when it exceeds 10 (Nguyen, 2011) In Table 4.6.1, the VIFs for 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 found to be 1.078, 2.191, 2.711, 1.782, 1.997, and 1.953, respectively Since all VIFs were below 10, this indicates that multicollinearity was not present among the predictor variables in the analysis.
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 coefficient (R) is 54.8% (R² = 0.548), while the Adjusted R Square is 0.538, demonstrating that 53.8% of the variability in LS can be predicted from the combined effects of IQ, LA, TC, IN, TQ, and CO.
The Anova table (table 4.8.1) indicated that F= 58.936 and was significant
The combination of predictors significantly predicted learning satisfaction (LS), as indicated by a p-value (Sig.) lower than 0.05 in Table 4.8.1 Consequently, this study concludes that the model effectively assesses student satisfaction in e-learning.
Teaching capability significantly enhances student satisfaction with e-learning in Vietnam.
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 of learner attitude towards technology revealed a value of \$\beta = 0.033\$, with a t-value of \$t = 0.798\$ and a significance level of \$sig = 0.425\$, which is greater than 5% This indicates that learner attitude did not exhibit a positive relationship with learner satisfaction, leading to the conclusion that the second hypothesis was not supported.
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
The test ultimately focused on hypotheses H3a and H3b The Coefficients table revealed significant p-values for both technology quality (TQ) and internet quality (IQ), indicating their importance in the analysis.
The study found a statistically significant relationship between TE, which encompasses TQ and IQ, and LS The beta values for TQ and IQ were 202 and 346, respectively, indicating that TE positively influences LS Therefore, both hypothesis 3a and hypothesis 3b were supported.
A regression analysis revealed that 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 crucial role in enhancing LS in Vietnam Conversely, learner attitude (LA) was found to have a lesser impact on overall learner satisfaction among the predictors.
The analysis revealed that LS exhibited the strongest positive correlation with IQ, indicated by a coefficient of ò = 0.346 and a p-value of 0.000, which is significant at the 5% level Additionally, LS demonstrated a strong positive relationship with TQ, as evidenced by a coefficient of ò = 0.202 and a p-value of 0.000 Furthermore, the correlation between LS and CO was also notably positive, with a coefficient of ò = 0.154 and a p-value of 0.018 In contrast, LA showed no statistically significant positive relationship with LS, as reflected by a coefficient of ò = 0.033 and a p-value of 0.425, which exceeds the 5% significance threshold.
In the study, LA, TC (with ò = 0.104 and p = 0.075) and IN (with ò = 0.098 and p = 0.063) did not show statistically significant positive relationships with LS Overall, TQ, IQ, and CO emerged as the significant predictors of LS.
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 re-identified the factors influencing student satisfaction with e-learning at universities in Vietnam, highlighting the importance of instructor capability—encompassing teaching skills, course organization, and student-instructor interaction—as well as the technology dimension, which includes technology quality and Internet quality The impact of each factor on e-learner satisfaction is illustrated in Figure 4.9.2.
Figure 4.9.2 The final research model
0.202 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
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 conducted a multicollinearity test to ensure accurate correlations among predictor variables The Variance Inflation Factor (VIF) for an independent variable is typically considered problematic if it exceeds 10 (Nguyen, 2011) In Table 4.6.1, the VIFs for 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 found to be 1.078, 2.191, 2.711, 1.782, 1.997, and 1.953, respectively Since all VIFs were below 10, this indicates that multicollinearity was not present among the predictor variables in the analysis.
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 coefficient (R) is 54.8% (R square = 548), with an Adjusted R Square of 538, demonstrating that 53.8% of the variability in LS can be predicted from the combined effects of IQ, LA, TC, IN, TQ, and CO.
The Anova table (table 4.8.1) indicated that F= 58.936 and was significant
The combination of predictors significantly predicted learning satisfaction (LS), as indicated by a p-value (Sig.) lower than 0.05 in Table 4.8.1 Consequently, this study concludes that the model effectively assesses student satisfaction in e-learning environments.
Teaching capability significantly enhances student satisfaction with e-learning in Vietnam.
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 of learner attitude towards technology revealed a value of \$\beta = 0.033\$, with a t-value of \$t = 0.798\$ and a significance level of \$sig = 0.425\$, which is greater than 5% This indicates that learner attitude did not exhibit a positive relationship with learner satisfaction, leading to the conclusion that the second hypothesis was not supported.
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
The test ultimately focused on hypotheses H3a and H3b The Coefficients table revealed significant p-values for both technology quality (TQ) and internet quality (IQ), indicating their importance in the analysis.
The study found a statistically significant relationship between TE, including TQ and IQ, and LS, with beta values of 202 for TQ and 346 for IQ This indicates that TE positively influences LS, supporting both hypothesis 3a and hypothesis 3b.
Summary of the Results
A regression analysis revealed that 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 crucial role in enhancing LS in Vietnam Conversely, learner attitude (LA) was found to have a lesser impact on overall learner satisfaction in the e-learning context.
The analysis revealed that LS exhibited the strongest positive correlation with IQ, indicated by a beta weight of \$\beta = 0.346\$ and a p-value of \$p = 0.000\$, which is significant at the 5% level Additionally, LS also demonstrated a strong positive relationship with TQ, with a beta weight of \$\beta = 0.202\$ and a p-value of \$p = 0.000\$ Furthermore, the correlation between LS and CO was notably strong, as evidenced by a beta weight of \$\beta = 0.154\$ and a p-value of \$p = 0.018\$ In contrast, LA showed no statistically significant positive relationship with LS, with a beta weight of \$\beta = 0.033\$ and a p-value of \$p = 0.425\$.
In the study, LA and TC (with coefficients of ò = 0.104 and p = 0.075, and ò = 0.098 and p = 0.063, respectively) did not show statistically significant positive relationships with LS Conversely, TQ, IQ, and CO emerged as significant predictors of LS.
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 redefined the factors influencing student satisfaction with e-learning at universities in Vietnam, identifying key elements such as instructor capability—which encompasses teaching skills, course organization, and student-instructor interaction—and the technology dimension, which includes technology quality and internet quality The impact of each factor on e-learner satisfaction has been predicted and is illustrated in Figure 4.9.2.
Figure 4.9.2 The final research model
0.202 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