Factors Affecting The Satisfaction Of Huflit Students With Online Learning.pdf

Keywords: Satisfaction, online learning intention, HUFLIT students, flexibility, interactivity, ease of use, usefulness technology skills, self-discipline, high discipline... Online lear

HO CHI MINH CITY UNIVERSITY OF FOREIGN LANGUAGES

- INFORMATION TECHNOLOGY FACULTY OF BUSINESS ADMINISTRATION

HCMC, 12/2022

Abstract

Satisfaction is an important factor that strongly influences students’ online learning intention This study uses primary research methods to find out the influence of satisfaction on the online learning intentions of HUFLIT students in Ho Chi Minh City According to the results of the focus group study, the survey participants said that flexibility, interactivity, ease of use, and usefulness are factors that influence the satisfaction that makes them intend

to study online Research suggests that educational institutions should focus on two main factors when applying online learning: developing technology skills for students and lecturers and developing online learning systems and software For students, it is necessary

to set goals, study methods, improve technology capacity, and practice self-discipline and high discipline

Keywords: Satisfaction, online learning intention, HUFLIT students, flexibility, interactivity, ease of use, usefulness technology skills, self-discipline, high discipline

Introduction

The eruption of digital technologies a few decades ago, the conventional character of educational processes underwent changes (Ahern & Repman, 1994) Online or

communication, interaction, and the delivery of multimedia materials that enhance learner-directed learning as a result of the emergence of Internet commercialization and the proliferation of information technologies (Wu et al., 2008) Additionally, COVID-19 is a component that encourages the education sector's rapid digital transition COVID-19 is a contagious disease that is infectious and spreads quickly among people and was discovered in Wuhan, China, in December 2019 (Gopal et al., 2021) More than 850 million pupils worldwide had their courses suspended due to COVID-19, which caused the original lesson plans of the schools in these countries and regions to be disrupted (Chen et al., 2020)

Vietnam is not exempt from COVID-19's impacts The first COVID-19 epidemic in Vietnam at the end of January 2020 prompted the government to decide to close all educational establishments (Maheshwari, 2021) In response to the outbreak of the epidemic, the online classroom has emerged as a crucial tool for preserving regular academic order in the wake of the epidemic's outbreak (Chen et al., 2020) Schools and universities were then instructed to transition their teaching to an online format (Pollack, T., Thwaites G., & Rabaa, 2020) Online learning is gradually becoming popular in education methods across the country The term "online learning" is frequently used in the context of higher education to refer to courses that are entirely delivered online (Crawford et al., 2014; Graham, 2006) Online learning can be broadly characterized as the use of the internet to improve student-teacher engagement Online delivery includes both synchronous and asynchronous types of engagement, such as chat rooms, email, newsgroups, and conferencing tools Asynchronous forms of interaction include evaluation tools and the provision of web- based course materials It covers both in-person instruction and online learning options (Singh & Thurman, 2019) "Web-based education" and "e-learning" are other words that are interchangeable with online learning (Curtain, 2002) Online learning offers students convenience, ease of access, and flexibility in participation (Croxton, 2014); additionally to better student accessibility, higher degree completion rates, and non-traditional students’ appeal of online courses (Bolliger & Wasilik, 2009) Academic leaders in the United States reported that the demand for online courses or programs has increased more than that for in-person courses, indicating that online learning is essential to the long-term growth of their institutions (Kuo et al., 2013) Studies from the past have shown that the learning results of online learning are not significantly different from those of traditional face-to-face classroom instruction (LI

E Allen & Seaman, 2010; M Allen et al., 2002; Biner et al., 1997; Brown &

1

Liedholm, 2002; Scott D Johnson et al., 2000) When compared to face-to-face training, the level of student satisfaction with online learning is unaffected (I E Allen

& Seaman, 2010) One of the key factors that higher education institutions believe determines the quality of their programs in today's marketplace is student satisfaction (Yukselturk & Yildirim, 2008), due to the fact that student satisfaction is regarded as

a crucial indicator for evaluating the value of academic experiences (Kuo et al., 2013; Yukselturk & Yildirim, 2008) How students feel about their educational experience

is reflected in their satisfaction with it (Alqurashi, 2019) Research has established the significance of student satisfaction with online learning and showed strong correlations between it and students' success rates, determination, motivation, and commitment to finishing a degree online (Ali et al., 2011; DeBourgh & Gregory, 1999; Yukselturk & Yildirm, 2008) Students are more motivated to continue studying when they are more satisfied with their educational experience (Johnson et al., 2008) The nature of online learning necessitates more responsibility for students than traditional classroom education does (Thorpe, 1997) Online students who are unable to effectively control their learning are unlikely to be satisfied (Artino, 2007; Puzziferro, 2008) The efficacy of the Internet, interaction, lecturers, students, training programs, and self-control were all elements this study looked into in relation

to student satisfaction in a totally online learning environment

It should be mentioned that prior research on the quality of online learning services was mostly undertaken in developed nations, leading to various online learning service quality attributes (Dursun et al., 2020; Machado-da-Silva et al., 2014; Martinez Argiielles et al., 2013) Vietnamese higher education 1s becoming more and more connected with the global education system (Welch, 2010) However, studies on this issue in Vietnam are still limited

The goal of the study is to get an overview of HUFLIT student satisfaction with online learning The study goals may be more precisely defined as follows: (1) To ascertain the factors influencing HUFLIT students’ satisfaction with online learning; (2) Identify the importance of factors and ways to increase HUFLIT student satisfaction with online learning; (3) investigate the relationship between HUFLIT students’ satisfaction and success in online learning

In order to achieve the purposes of this study, the following questions must be answered: (1) What factors affect the satisfaction of HUFLIT students with online learning? (2) What are the important factors and ways to increase HUFLIT student satisfaction with online learning? (3) Is there a relationship between HUFLIT student satisfaction and online learning success?

The scope of this study is limited to HUFLIT students who are still studying at HUFLIT A university in Ho Chi Minh City, Vietnam

II Literature review

(Amoroso & Cheney, 1991; Rivard, 1987) There will therefore be more learning

effects the higher the quality and dependability of IT (Pelc, 1996; Piccoli et al., 2001; Webster & Hackley, 1997) The use of other technology, such as video conferencing, for learning and discussion may also be part of online learning (Isaacs et al., 1995) Consequently, the success of online learning depends on both the quality of technology and the Internet (Piccoli et al., 2001; Webster & Hackley, 1997)

Hypothesis 1 Internet quality has a positive impact on the satisfaction of HUFLIT students with online learning

Flexibility

The engagement and satisfaction of online learning students are facilitated by the flexibility of online learning courses in terms of time, place, and techniques (Arbaugh, 2000; Berger, 1999; Leidner & Jarvenpaa, 1995) The removal of physical obstacles also makes it possible for more dynamic contact, which encourages the development of constructive learning and opportunities for cooperative learning (Brandon & Hollingshead, 1999; Salmon, 2000) Students may communicate instantly, anytime, and anywhere using online learning because there are no time or space constraints (Leidner & Jarvenpaa, 1995; Taylor, 1996) Additionally, because it is virtual, it removes the awkwardness that comes with face-to-face interaction in conventional classrooms (Sun et al., 2008) Through discussion boards or other bulletin board systems, students can ask questions and share their opinions without feeling self-conscious (Straus, 1996) The meaning of

an online course is the effectiveness and results of using online Learning in learners’ working, learning, and commuting hours are perceived as flexible by learners (Sun et al., 2008)

Hypothesis 2 Perceived flexibility has a positive impact on the satisfaction of HUFLIT students with online learning

Interaction

H.4

ILS

According to Arbaugh (2000), online learning satisfaction increases when learners see their interactions with others more Interactions between students and instructors, other students, or course materials can help students grow and overcome difficulties in a virtual learning environment (Sun et al., 2008) Electronic communication could enhance learning outcomes (Piccoli et al., 2001) Numerous academics concur that successful learning depends heavily on interactive instructional design (Hong, 2002; Jiang & Ting, 1998; Nahl, 1993) According to Moore (1989), in learning activities, students can interact with teachers, with resources, and with other students in one of three ways Learning activities are significantly influenced by teaching methods, particularly by interactions between teachers and students (Webster & Hackley, 1997) Without obvious interactions between teachers and students, students are more likely to become distracted and have trouble focusing on the course materials (Isaacs et al., 1995) Online learning can take place practically anywhere, thus 1t demands more focus than traditional face-to-face encounters (Kydd & Ferry, 1994) To increase the frequency, quality, and promptness of interactions that may have an impact on learner satisfaction, interaction mechanisms in online learning environments should be effectively developed (Sun et al., 2008)

Hypothesis 3 Perceived interaction has a positive impact on the satisfaction of HUFLIT students with online learning

Ease of use

When a person thinks utilizing a piece of technology won't need much effort, they have Perceived Ease of Use (PEU) (Davis, 1989) The degree to which someone perceives a system to be easy to use is referred to as perceived ease of use The term "perceived ease of use” refers to how users believe a system to be to use There are several obstacles to the effective use of technology in online classes, including internal characteristics like self-efficacy and personal ideas about utilizing technology (Brinkerhoff, 2006; Teo & Noyes, 2011) Technology use suffers from a lack of self-efficacy and perceived ability, which deters instructors and students from using it into their instruction and learning (Yan & Piper, 2003) Hypothesis 4 Perceived ease of use has a positive impact on the satisfaction of HUFLIT students with online learning

Usefulness

The degree to which a person thinks employing a certain technology would improve his or her ability to accomplish their work is known as perceived usefulness While usability is more closely linked to the interface and information delivery, usefulness is tied to the caliber of the material (Laub, 1999) It was discovered that perceived usefulness and users’ intents to use technology are

4

H.6

related (Venkatesh & Morris, 2000) Another study's findings indicate that intrinsic variables like self-efficacy have a direct impact on students' desire to utilize technology (Yi & Hwang, 2003) Online learning's perceived effectiveness

is thought to be related to intrinsic variables Perceived usefulness was determined by questions about how effective online learning was at piqueing students’ interest in the subject and helping them comprehend it better (Maheshwari, 2021) Therefore, perceived usefulness and mtrinsic characteristics may have an impact on learners’ intents

Hypothesis 5 Perceived usefulness has a positive impact on the satisfaction of HUFLIT students with online learning

Satisfaction

Student satisfaction is referred to as the student's estimation of the worth of his or her educational experiences at a facility (Feldman & Astin, 1994) There are still big disparities in how students see their online learning experiences (Muilenburg

& Berge, 2005) Students’ perceptions of their learning experiences can have an impact on whether they decide to enroll in the course or not (S Carr, 2000) and influence of general levels of satisfaction with online learning (Kenny, 2003) The American Distance Education Consorttum (ADEC, n.d.) claims that student satisfaction is "the most crucial key to ongoing learning” (Tikly, 2010)

According to Muilenburg and Berge (2005), there are a number of obstacles that students face when studying online Administrative obstacles, social contact, academic and technical skills, motivation, time, restricted access to resources, and technical challenges are only a few of these roadblocks Other obstacles include new duties and responsibilities, teacher response delays, a lack of technical support, a strong reliance on technology, and poor student performance and satisfaction (Flanagan, 2000) The capacity to achieve in an online learning environment must be believed by the pupils Indicators of student performance include student satisfaction

Hypothesis 6 Perceived satisfaction has a positive impact on the satisfaction of HUFLIT students with online learning

For the purposes of any research, primary data refers to information and data that are directly acquired (C.R.Katharin, 2004) The fact that researchers are gathering data for their particular research aims is one benefit of using primary data To gather information pertinent to the study's context—online learning—the researchers’ questions in this one were modified Consequently, the author will have information and provide responses via an online survey The primary one can be acquired through a variety of methods, including focus groups, interviews, phone interviews, and online questionnaires Among the options available, the author selected an online questionnaire survey since it can swiftly reach focus groups and provide participants with ease and flexibility

The three main research methodologies are the experiment, observation, and questionnaire Specifically, questionnaires are frequently used to gather primary data (Kumar Ranjit, 2019) It is a mechanism for gathering information from a broad sample of the population rather than concentrating on a single person Additionally, the survey is quick, easy to complete, and produces a lot of data

6

quickly (Kelley et al., 2003) It is also useful for surveys of attitudes and behaviors because it is generally accurate and dependable On the basis of these arguments, the survey can then be utilized to gather data for this study

11.2 Sampling method

Non-probability judgment sampling method was used to select the research sample In judgmental sampling, researchers select samples based on their expertise to complete the task in a way that ensures that all participants have the same characteristics (Taherdoost, 2016) It applies whenever respondents are asked about whether they have expertise in using a particular product or performing a particular action (Alchemer, 2018) Since the study participants are HUFLIT students and need smartphones to access the Internet, it is important to choose the appropriate assessment form Therefore, in this investigation, non- probability sampling with judgment was used

TIL3 Sample size

The 10-times rule is one of the usual sample techniques that may be stated (Rigdon et al., 2017) The sample size of PLS-SEM studies should, in accordance with this methodology, be "equal to the larger number usually times the maximum number of formation indices used to measure a single structure, or ten times the number of largest structural paths directed at a particular structure in the structural model” (Rigdon et al., 2017) There are six hypotheses in the study model for this article that points to five possible routes to HUFLIT student satisfaction with online learning Consequently, 50 is the recommended minimum size The sample size in this study—221—clearly surpassed 50

Daniel Soper's technique for sampling may also be widely used When determining the sample size for this study, Daniel Soper (Westland, 2010) was also taken into consideration This technique is a simple to use online learning tool The number of observed and latent variables in the model will be 25 and 7, respectively, with default values of 0.30, and the desired probability and statistical energy levels will be 0.05 and 0.8, respectively Daniel Soper will use structural equation modeling (SEM) to determine the necessary sample size for this study Thus, 221 replies were included in the entire sample pool that could be used for data analysis, exceeding the minimal sample size requirement of 170 TIL.4 Questionnaire design

The survey asks about how HUFLIT students feel about online learning and their satisfaction with it Prior to the main study, a pilot study with 30 people with prior expertise in online learning was conducted to improve the questionnaire's reliability and validity The original questionnaire had a few small changes made

to it before it was formally released The questionnaire was then correctly and

readily translated into Vietnamese for the respondents to complete The survey was modified from earlier research Based on validated and trustworthy scales that could be found in the literature, the survey questionnaire was created Every item was scored on a seven-point Likert scale, with 1 being the strongest disagreement and 7 being the strongest agreement The following table presents the measurement elements and sources, as shown in Zable 3.1

Table 3.1 Questionnaire structures

Internet

IQ2 I feel the fee to connect to the Internet is

very cheap

1Q3 I feel it's easy to go online

(Sun et al, 2008)

Perceived

flexibility PF1 Taking this class via the Internet allowed me to arrange my work for the class more

effectively

PF2 Taking this class via the Internet allowed

me to spend more time on non-related activities

PF3 There were no serious disadvantages to

taking this class via the Internet

PF4 Taking this class via the Internet saved me

a lot of time commuting to class

(Arbaugh, 2000)

Perceived

interaction PI1 The instructor frequently attempted to elicit student interaction

PI2 I felt that the quality of class discussions

was high throughout the course

PI3 It was easy to follow class discussions

PI4 Classroom dynamics were not much

different than in other courses

(Arbaugh, 2000)

Perceived

usefulness

enhance my effectiveness in the program

PU2 Using web-based learning system would

improve my performance in the program

PU3 I would find web-based learning system

useful in the program

program would enhance my productivity

Perceived | PEUI It would be easy for me to become | (Arbaugh,

PEU2 Learning to operate web-based learning systems would be easy for me

Perceived | PSI I am satisfied with my decision to take this | (Arbaugh,

PS2 I was very satisfied with the course

Internet as I can

IILS Data analysis techniques

The partial least squares (PLS) approach was utilized to process and analyze the data Because the PLS technique establishes minimal constraints for scale, sample size, and residual distribution, it is appropriate (Monecke & Leisch, 2012) Hair et

al (2014) claim that since the 2000s, as the population has grown, there have been more published research utilizing PLS-SEM Particularly in the areas of

management, and marketing research on satisfaction analysis because PLS-SEM has an advantage over CB-SEM in the following circumstances: (1) prevents issues with limited sample numbers and non-delivery data standards; (2) is capable of estimating complicated research models with numerous intermediate, baseline, and observed variables, particularly structural models; and (3) is consistent with prediction-oriented investigations (Henseler et al., 2014) The PLS-SEM method is used in this study to analyze the effect of e-assessment on HUFLIT students' satisfaction with online learning To gather information from respondents on how they view the measurements, a Likert scale with seven-point

response possibilities from "(1) Strongly disagree” to "(7) Strongly agree” was employed

HH.6 Assessing the outer measurement model

Prior to evaluating the hypotheses in the internal model (structural model), it is recommended to evaluate the external model (measurement model) This includes testing the convergence and discriminant validity of the measurement model as well as its reliability (Cronbach's Alpha and composite reliability)

Table 3.2 shows that the Cronbach's Alpha values for internally consistent dependability, a metric of structural reliability, are all higher than the suggested cutoff value of 0.70 (Hair Jr et al., 2016) However, the findings in the same table also show that the composite confidence values are greater than the necessary value of 0.70 set by (Hair Jr et al., 2016) As a result, all constructions exhibit a considerable level of dependability, and both the composite reliability and the fundamentally consistent reliability of Cronbach's alpha are proven

The evaluation of multiple conceptually related things is referred to as

"convergent validity." According to Hair Jr et al., (2016), the average variance extracted (AVE) is suggested to evaluate the convergent validity, and its value must be larger than 0.50 before it can be said to be validated All AVEs are considerable and greater than 0.50, as shown by Table 3.2 The value of outer loadings, on the other hand, may serve as a means of confirming convergent validity (Hair Jr et al., 2016) Accordingly, the convergent validity would be confirmed if the outer loadings value 1s larger than 0.70 It is clear from the data

in Table 3.3 that all values are higher than 0.70 This study's convergent validity has been verified as a result

Table 3.2 Overview of measurement model quality

Table 3.3 Outer loadings of the measurement model

11

PE

0.819 0.851 0.708 0.732

PI

1.000

PS

0.773 0.831 0.822 0.852

PU

0.769 0.898 0.876 0.744