Students’ perception on online learning during covid pandemic a case study on the students of banking university of ho chi minh city 2021

MINISTRY OF EDUCATION AND TRAINING THE STATE BANK OF VIET NAM BANKING UNIVERSITY OF HO CHI MINH CITY PHẠM LÊ KHẢ TRÂN STUDENTS’ PERCEPTION ON ONLINE LEARNING DURING COVID PANDEMIC A CASE STUDY ON THE.

PHẠM LÊ KHẢ TRÂN STUDENTS’ PERCEPTION ON ONLINE LEARNING DURING COVID PANDEMIC: A CASE STUDY ON THE STUDENTS OF BANKING

UNIVERSITY OF HO CHI MINH CITY

BACHELOR THESIS PROPOSAL MAJOR: BUSINESS ADMINISTRATION

CODE: 0308050007

HO CHI MINH CITY, 2021

BANKING UNIVERSITY OF HO CHI MINH CITY

PHẠM LÊ KHẢ TRÂN STUDENTS’ PERCEPTION ON ONLINE LEARNING DURING COVID PANDEMIC: A CASE STUDY ON THE STUDENTS OF BANKING

UNIVERSITY OF HO CHI MINH CITY

BACHELOR THESIS PROPOSAL MAJOR: BUSINESS ADMINISTRATION

HO CHI MINH CITY, 2021

The purpose of this study is to address some main factors that affect students’ perception

on online learning and discuss its potential to make it more inclusive and comprehensive One of the objectives of this study is assessing the level of influence of each factor toward perception This goal has been followed by using a model examining the impact of Motivation, Perceived Usefulness, Perceived Ease of Use, Instructor, Interaction and Academic Integrity as the hypotheses of study A sample of 119 students of Banking university of Ho Chi Minh city have participated in this study The respondents' answers were tested through the use of Cronbach's Alpha and Exploratory Factor Analysis (EFA) Finally, regression analysis was used on data in order to test hypotheses of study The results show that Motivation, Perceived Usefulness, Interaction and Academic Integrity are factors affecting online learning perception, in which Motivation has the most influence This thesis serves as a theoretical basis about the perception on Banking University students toward online learning for researchers, academic staff in Vietnam who want to do further research in this area From there, some recommendations are made

to help expanding online learning offerings in the post-crisis period

Keywords: COVID-19, online learning, perception, motivation, perceived usefulness,

interaction, academic integrity

I declare that all material presented in this paper is my own work or fully and specifically acknowledged wherever adapted from other sources

I understand that if at any time it is shown that I have significantly misrepresented material presented here, any degree or credits awarded to me on the basis of that material may be revoked

I declared that all statements and information contained here are authentic, correct and accurate to the best of my knowledge and belief

Ho Chi Minh City, June 2021

Pham Le Kha Tran

I am extremely thankful for my supervisor Bui Duc Sinh for his careful guidance, support, encouragement and enthusiasm during my thesis research

I would also like to thank Banking University of Ho Chi Minh City for offering the opportunity to complete this project

Due to my limited knowledge and ability, there are still many shortcomings and limitations I hope for the guidance and contributions of teachers to make my thesis more complete Sincerely, thank you!

Ho Chi Minh City, June 2021

Pham Le Kha Tran

CHAPTER 1: INTRODUCTION 1

3.2.3 Data collection 24

3.3.2 Correlation coefficient and logit regression analysis 26

4.3 Analysis exploratory factor analysis (EFA) 32

Abbreviations Full meaning

TAM Technology Acceptance Model

EFA Exploratory Factor Analysis

MO1 I lose motivation when I study online

MO2 I get distracted while studying online

MO3 I often can't keep up with the lectures when I study online MO4 I lack motivation to do individual and group exercises MO5 I lack motivation to do individual and group exercises PU1 Online learning saves time

PU2 Online learning saves money

PU3 Online learning is very flexible

PU4 Online learning helps me improve my computer skills

PEU1 It's easy for me to use online learning apps

PEU2 I quickly mastered online learning applications

PEU3 I rarely have technical problems when I study online

PEU4 I rarely lose my internet connection when I study online

IN1 Instructor has good technology skills

IN2 Lecturers often interact with students

IN3 Instructor is ready to assist students when needed

IN4 Lecturers design good lesson content

IN5 Teachers motivate and interest students to learn

IR1 Stress-free online learning environment

IR2 I am less afraid to communicate with teachers

IR3 I ask more questions via the comment function

IR4 I am more confident when presenting online

IR5 I am more comfortable participating in discussions when I study

online AI1 I think students tend to cheat more when studying online

AI2 I feel cheating is more common when learning online

AI3 I helped my classmates cheat when taking an online test

AI4 I often can't keep up with the lectures when I study online

PE1 I feel like I have to cheat to be able to compete with others

PE3 I have a positive view of online learning

PE4 I find online learning very interesting

PE5 I love learning online

Figure 1: Technology Acceptance Model (Davis, 1989) 7 Figure 2: Factors that influence perception (adapted from Robbins, 2001, by Bergh &

Figure 3: The model of factors impacting on online learning perception 13 Figure 4: The modified model of factors impacting on online learning perception 33

Table 1: Summary of previous studies 8Table 2: Summary table of hypothesis research of the model is exported 18

Table 5: Reliability test of variable Perceived Usefulness (first time) 25Table 6: Reliability test of variable Perceived Usefulness (second time) 26Table 7: Reliability test of variable Perceived Ease of Use 26

Table 10: Reliability test of variable Academy Integrity 27Table 11: Reliability test of variable Perception (first time) 28Table 12: Reliability test of variable Perception (second time) 28Table 13: KMO and Bartlett's Test for Independent Variable 29Table 14: Total Variance Explained for Independent Variable 30Table 15: Factor loading for Independent Variable Rotated Component Matrix 30Table 16: KMO and Bartlett's Test For Dependent Variables 31Table 17: Total Variance Explained for Independent Variable 31

Table 19: Groups of factors after performing exploratory factor analysis 32

Table 23: Regression results of each independent variable 36

1.1 Background and significant

The year 2019 saw an outbreak of CoronaVirus (SARS-CoV-2), which led to the deadly pandemic that threatened the whole of humanity In an attempt to reduce the transmission

of COVID-19, many restrictive policies have been implemented to prevent crowding, including social-distancing, self-isolation and the near-total shutdown of factories, restaurants, stores, schools and so on In February 2020, Vietnamese Ministry of Education and Training issued an order to suspend all school operations nationwide as part of quarantine measures As the Covid-19 pandemic was becoming more complicated, the nationwide school closure has been continuingly sustained The extension of school closures challenged the education system across the country and forced many institutions that previously did not want to change their traditional approach, with no choice but to switch back all to online teaching and learning With the motto “Stop going to school but don't stop studying”, the Ministry of Education and Training has provided guidance on implementing online teaching and learning for the entire education system towards IT application, but still must ensure Basic quality standards in training in mid-March 2020 Universities have quickly adapted to develop their digital tools and platforms to ensure uninterrupted educational delivery to their isolated students On the other hand, many online learning platforms are providing free access to their services in response to significant demand As with most other teaching methods, online learning also has its own positives and negatives

COVID-19 while posing a danger to mankind, has grown organizations to invest in online learning While traditional, on-campus learning will inevitably return to prominence as the coronavirus subsides, universities can use this crisis as an opportunity to learn more about new digital tools and how to best leverage them Addressing these issues could contribute to creating strategies for delivering lessons more effectively, expanding online learning offerings in the post-crisis period Because of the inevitability of online learning

in the future, there is an urgent need to investigate students’ perception on online learning

during COVID pandemic This thesis aims to address some main factors that affect students’ perception on online learning and discuss its potential to make it more inclusive and comprehensive

1.2 Research Objectives and Questions

1.2.1 General objective

The subjects of this research are factors affecting the perception on Banking University students of online learning during COVID pandemic

1.2.2 Specific objective

The purpose of this paper is to:

- Determine the factors affecting the perception on online learning of University Banking students

- Evaluate the influence of each factor

- Propose implications for the success of online mode of learning

The degree of influence of factors on the perception on online learning?

What are the implications that help Banking University to increase the quality of online learning platforms and services?

1.3 Contribution of the thesis

Theoretically:

The thesis serves as a theoretical basis about the perception of Banking University students toward online learning for researchers, academic staff in Vietnam who want to

do further research in this area

1.4 Research scope and subject

The scope of the thesis focuses on researching the factors that influence the perception on online learning during COVID pandemic regarded by students studying at the Banking University of Ho Chi Minh City

1.5 Structure of the thesis

The thesis consists of 5 chapters:

Chapter 1: OVERVIEW OF THE RESEARCH THESIS

Chapter one focuses on presenting an overview of the research topic by asking questions, stating the reasons for implementing the topic, the scope of the research object, the contribution of the topic and the research objective The above contents provide an overview of the content of the topic and create a basis for understanding the theoretical basis in the next chapter

Chapter 2: THEORETICAL BASIS AND RESEARCH MODEL

Chapter two presents perception concepts, theoretical models and prior studies These theories are the foundation for proposing a research model of factors affecting the student's Banking University students’ perception on online learning

Chapter 3: RESEARCH METHODS

Chapter three focuses on research methods including study design, sampling and statistical data analysis techniques

Chapter 4: RESEARCH RESULTS

Chapter four presents the results of the study after analyzing the data The tool used for analysis is SPSS 20 software

Chapter 5: IMPLICATIONS

Chapter five summarizes research results and proposes governance implications to contribute to improving the effectiveness of online mode of learning At the same time, the limitations of the research paper will also be mentioned in this chapter

CHAPTER 2: THEORETICAL BASIS AND RESEARCH MODEL

Chapter two presents perception concepts, theoretical models and prior studies These theories are the foundation for proposing a research model of factors affecting the student's Banking University students’ perception on online learning

2.1 Relevant concepts

2.1.1 Online learning

Along with the rapid advancement of technology, online learning is becoming an increasingly significant trend Even before the sudden appearance of Covid, the online learning system has already been implemented in various locations The number of online courses continues to escalate steadily and accelerate with the unexpected presence of the pandemic In the literature, online learning does not have a generic definition because of the overload of explanations and descriptions developed by many researchers and authors According to Retnoningsih (2017), online learning is defined as a study process that is facilitated and supported by taking advantage of information and communication technology In a similar fashion, Saifuddin (2017) also describes online learning as a distance learning that connects students with their learning resources as well as others through the use of the internet Among many authors, Solomon Negash and Marelene V Wilcox (2008) offered the most complete explanation of online learning, as is a real-time presence where the instructor and learner are both present at the time of learning content delivery The applications in online learning can be different and diverse depending on each place As stated by Fauzi & Khusuma (2020), universities are required to adapt online teaching by carrying out the implementation of various offered applications, such

as the zoom application According to Dewi (2020), various applications can be utilized

to support the interaction in online learning, including classroom, video conference, zoom and so on In brief, online learning can be defined as a learning approach that exploits the

potential of the Internet and technology in order to provide and receive educational content

2.1.2 Perception

Similar to online learning, perception is also a term that carries many explanations from many authors As reported by Hermawan & Tyas (2018), perception is the stage of knowing the environment such as objects, people, and symbols or signs that requires the recognition process Kreitner and Kinicki (1992) explain perception as “a mental and cognitive process that enables people to interpret and understand the surroundings” However, the most popular and widely accepted definition of perception is given by Schacter & Daniel (2011), in which perception is described as the organization, identification and interpretation of sensory information for the purpose of figuring out the information presented or the environment To conclude, perception is the way people interpret or respond to whatever is happening around them Consequently, this process can be influenced by internal and external factors While internal factors include individual experience, motivation and expectation; external factors involve others’ expectations, cultural norms and society

The perception process consists of three stages, which are selection, organization, and interpretation Selection refers to people's ability to detect stimuli in the environment and convert them into meaningful experience The second stage is organization, in which people sort out the selected stimuli into categories according to meaningful patterns Organization is the final stage of perception, relating to the process of attaching meaning

to the selected stimuli In this stage, people with different experience, different characteristics will interpret the same object or event in different ways Which also means that people coming from similar backgrounds, similar cultures will have similar perspectives when regarding the same object

There are two dimensions of perception, namely the physical and the psychological The Physical Dimension of Perception refers to the similar way people regard the object and environment around them since they have the same sensory organs as eyes, ears, and nose The Psychological Dimension is considered to have a greater impact on perception because of the differences in people’s beliefs, motivation, attitudes, interests and cultural values

Human perception, to a certain degree, is affected by the factors belonging to three sources – the perceiver, the perceived and the situation Factors in the perceived object including appearance, size, motion and sound can impact the perception toward that object The situation factors refer to the context in which the objects are observed Factor

in perceiver relating to personal characteristics such as attitudes, motivation, interests, past experiences, and expectations It means that a person's perspective of an object is highly influenced by his or her experience, habits, motives, personality and personal values

Figure 2: Factors that influence perception (adapted from Robbins, 2001, by Bergh & Theron, 2003)

2.2 Technology Acceptance Model - TAM

There exist many theories about the explanation of user behavior toward based products and services The Technology Acceptance Model (TAM), developed by Davis (1989), is definitely the most prominent among others TAM was designed to predict the usage and adoption of new technology The purpose of this model is that it helps developers to evaluate the level of user-friendliness of the product and assess the potential of users In this model, the two components as perceived usefulness and perceived ease of use hold a certain influence on the users’ adoption of a new technology system

technology-However, TAM only provides general information about the technology adoption by users As a result, further information is required when applying TAM in specific fields,

so that the progression of technology can be navigated in the right direction (Mathieson, 1991) In recent years, the TAM model has been expanded by a number of researchers and has been applied to many different technologies including e-learning (Cheung & Vogel, 2013) Many other studies also discovered that perceived usefulness and perceived ease of use have a significant impact on students’ acceptance of e-learning (Bures et al

2002, Selim 2003, Ong et al 2004, Drennan et al 2005, Saade & Bahli 2005)

Figure 1: Technology Acceptance Model (Davis, 1989)

2.3 Previous researchs

The research “A Preliminary Study of Business Student Perceptions of Online versus Face-to-Face Education” by Lynn A Fish and Coral R Snodgrass focuses on student characteristics (age, gender or familiarity with online courses) and program characteristics (academic rigor or the ease of cheating) to assess the value of online versus face-to-face courses The result shows that Difficulty, Motivation, Student interaction and Instructor interaction are identified as the infavorable factors of online courses Cheating is perceived as likely to be more happen in online commynity than traditional environment Students also indicate that as they gain more online experience, their perceptions of online education improve

The article “Integrating students’ perspectives about online learning: a hierarchy of factors” by Montgomery Van Wart and co reports seven factors such as Basic Online Modality, Instructional Support, Teaching Presence, Cognitive Presence, Online Social Comfort, Online Interactive Modality, and Social Presence as critical for the success of online learning According to the research, students value the basics of a course first, next are technological and instructor competence, then engagement and virtual comfort and final is social presence

In the paper “Extending the TAM model to explore the factors that affect Intention to Use

an Online Learning Community”, Liu and co take the Technology Acceptance Model as

a foundation and add new variables, such as Online Course Design, User-interface Design, Previous Online Learning Experience, and Perceived Interaction to explore whether users are willing to adopt an online learning community The research result shows that all the hypotheses are supported and all the extended variables can effectively predict whether users will adopt an online learning community

Table 1: Summary of previous studies

Author(s)

and year Research title

Research scope Methodology Results/Findings Van Wart,

perspectives about online learning: a hierarchy of factors

987 students enrolled in educational programs at Jack H Brown College of Business and Public

Critical success factor

methodology

The study reveals that Basic Online Modality,

Instructional Support, Teaching Presence,

Cognitive Presence, Social

Administration (JHBC),

California State University San Bernardino (CSUSB)

Online Comfort, Interactive Online Modality, and Social Presence are factors related

to quality from a student’s

perspective Liu, I F.,

to explore the factors that affect

Intention to Use an Online Learning Community

436 senior high school students from all over Taiwan

Structural equation modeling (SEM)

Online Course Design, User-interface Design, Previous Online Learning

Experience, and Perceived

Interaction have certain impact on Intention to Use

Learning Community Fish, L A.,

&

Snodgrass,

C R (2014)

A Preliminary Study of Business Student Perceptions of

67 students from Catholic University

Qualitative method

Student responses

to difficulty, motivation,

discipline, cheating, self-

Online versus Face-to-Face Education

directed learning, independence, and interaction with the instructor and other students

education as being preferred

(Soure: Author summarizes)

2.4 Hypothesis

2.4.1 Motivation

As reported by Golladay et al (2000) and Serwatka (2003), online learning requires a significant amount of discipline and self-motivation In addition, Allen & Seaman (2013) suggest that the success of online learning requires commitment from both sides, students must possess greater discipline, and teachers have to put more effort into delivering instructions Many researchers also report that learner motivation is the critical factor affecting students performing and playing an important role leading to the success of online learning (Cole, Field & Harris, 2004; Ryan, 2001) However, it is noted that self-discipline, self-motivation; and the time commitment to learning are some of the most obvious problems of online learning (Golladay, Prybutok, & Huff, 2000) Many studies yield mixed results, Kearsley (1998) reports that student motivation and self-esteem increase, while Maltby & Whittle (2020) states that it decreases

H1: Motivation has influence on student’ perception on online learning

2.4.2 Perceived usefulness

Perceived usefulness is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis 1989) As confirmed

by Saga & Zmud (1994), technology will be adopted if it is regarded as convenient, useful and socially desirable even though the using process is not enjoyable

Because of the close attachment between technology application and online learning, the TAM model will play a major role contributing to the success of online learning It implies that students will be more likely to have positive feelings about online learning when they find the system to be a useful tool that can boost their productivity as well as effectiveness

In the context of this thesis, online learning has been promoted as being more cost effective, convenient, and flexible For example, it has been proved that online learning allows students time to reflect on the learning materials, which encourages them to work

at their own pace (Richardson & Swan, 2003; Swan et al., 2000) In fact, perceived usefulness was already studied as the measurement of e-learning systems’ success by Joo

et al (2011), and Hsieh and Cho (2011) The studies above clearly make evident that perceived usefulness is a valid factor to measure the attitude toward online learning H2: Perceived Usefulness has influence on student’ perception on online learning

2.4.3 Perceived ease of use

Perceived Ease of Use is defined by Davis (1989) as “the degree to which a person believes that using a technology will be free from effort” Liu et al claims that perceived ease of use has a direct positive effect on the intention to use the system It is based on the reason that people will be more willing to learn how to use the system and continue to use

it if they perceive the system as relatively easy to use Other researchers, such as Chiu & Wang, (2008) also point out perceived ease of use is positively associated with the intention of continuing Web-based learning In the context of this study, perceived ease of use refers to the extent to which students believe that their continued use of online learning is free of effort

H3: Perceived Ease of Use has influence on student’ perception on online learning

2.4.4 Instructor

According to Finaly-Neumann (1994) and Williams & Ceci (1997), instructor are the main predictor in student satisfaction Differ from face-to-face classes, an online learning environment requires instructor to play many roles in order to guide students to success Goodyear, Salmon, Spector, Steeples, and Tickner (2001) suggest a model including eight roles for the online instructor, those of content facilitator, technologist, designer, manager/administrator, process facilitator, adviser/counselor, assessor and researcher Moreover, DeBourgh (1999) and Hiltz (1993) claim that student satisfaction has a strong positive correlation with the performance of the instructor, specifically with his or her availability and response time Moore & Kearsley (1996) state that instructor must be flexible and available when students have questions Hara & Kling (1999) and Vonderwell (2003) also emphasise that feedback is a key factor that heavily influences students’ satisfaction with online courses and they will feel stressed and frustrated when the feedback is delayed Not only to avoid frustration, on-time feedback can also keep learners involved and motivated (Smith & Dillon, 1999)

H4: Instructor has influence on student’ perception on online learning

2.4.5 Interaction

It is confirmed that interaction is a pivotal variable affecting student satisfaction toward distance learning environments (Bray, Aoki, & Dlugosh, 2008; Kuo, Walker, Schroder, & Belland, 2014; Rodriguez Robles, 2006) Many other researchers, such as Offir, Lev, and Bezalel (2008), also indicate that interaction level can be applied to anticipate the effectiveness of online classes Wegegrif, (1998) specifically points out that only when students feel being a part of a community of learners, they will be more likely to succeed

in studying online Richardson et al., (2015) further prove that students will feel more connected in the transactional learning space if they are able to maintain the interaction with the instructor report feeling

H5: Interaction has influence on student’ perception on online learning

Figure 3: The model of factors impacting on online learning perception

(Source: Author suggestion)

The model of this research is based on the TAM model with the addition of new variables carefully choosing from previous researchs Motivation, Student interaction and Instructor have certain influence on student’s perception on online learning (Fish & Snordgrass, 2014) Therefore, they are added to the model of factors influencing the perception on online learning Perceived Usefulness and Perceived Ease of Use are selected as they are proved to have impact on the will to adopt an online learning community (Liu & Chen,

2009) Furthermore, it is recognized that Academic integrity is crucial for the evalution of the value of online courses (Fish & Snordgrass, 2014) Based on the above observations, the proposed model includes Motivation, Perceived Usefulness, Perceived Ease of Use, Instructor, Interaction and Academic integrity in order to identify the factors that affect student’s perception on online learning

CHAPTER 3: METHOD OF THE RESEARCH

In chapter 3, the author will present research methods including research process, describe research sampling method, quantitative research, build expected scale and data analysis method

3.1 Research design

3.1.1 The method of building the scale

The paper has two research objectives related to creating a scale, including: measuring characteristics of students participating in the research and asking them to assess the level

of agreement with the impact of each factor to online learning perception

The nominal scale was built to distinguish and identify the study subjects The thesis has developed nominal scales including: Gender, Major, Program, School year and whether they have participated in any online classes The advantages of this scale are easy to set

up as well as high specificity and provide useful information

Hierarchical scales are designed to quantify and arrange problems in order, measuring attitudes, consciousness, opinions, interests and perceptions The scales and signs observed in the project use the Likert scale (5 levels) and are described in detail in a table

to identify the level of impact of each factor to online learning perception

Likert scale with five levels include: Strongly disagree, Disagree, Neutral, Agree, Strongly agree

The model has six scales of independent factors with 29 observed variables and a dependent factor scale with 5 observed variables built on a theoretical basis

Hypothesis H0 has 6 elements including: (1) Motivation, (2) Perceived Usefulness, (3) Perceived Ease of Use, (4) Instructor, (5) Interaction and (6) Academic Integrity

The dependent element is the perception on online learning

3.1.2 Research scale

The data source consists of secondary data and primary data

Primary data was collected through surveys The entire study was done using survey questionnaires because respondents were more likely to complete the questionnaire The questionnaire was posted on student groups of University in Ho Chi Minh City, Questionnaire Survey groups The responses from the respondents were obtained through the questionnaire which will be recorded via the email address of the researcher

Secondary data is collected from external sources such as books, journals, research articles and internet databases to provide information on theoretical basis, research models, research methods

Measurement model Motivation scale

The scale “Motivation” is based on Fish, L A., & Snodgrass, C R (2014) includes 05 observed variables coded from MO1 to MO5

MO1 I lose motivation when I study online

Fish, L A., & Snodgrass, C R (2014)

MO2 I get distracted while studying online

MO3 I get distracted while studying online

MO4 I often can't keep up with the lectures when I

The scale “Perceived usefulness” is based on Liu et al (2010 includes 05 observed variables coded from PU1 to PU5

PU1 Online learning saves time

Liu et al (2010)

PU2 Online learning saves money

PU3 Online learning is very flexible

PU4 Online learning helps me improve my

computer skills

PU5 The recording function helps me review the

lecture

Measurement model Perceived ease of use

The scale “Perceived ease of use” is based on Liu et al (2010 includes 04 observed variables coded from PEU1 to PEU4

PEU1 It's easy for me to use online learning apps

Liu et al (2010)

PEU2 I quickly mastered online learning applications

PEU3 I rarely have technical problems when I study

The scale “Instructor” is based on Van Wart et al (2020 includes 05 observed variables coded from IN1 to IN5

IN1 Instructor has good technology skills

Van Wart et al (2020)

IN2 Lecturers often interact with students

IN3 Instructor is ready to assist students when

needed

IN4 Lecturers design good lesson content

IN5 Teachers motivate and interest students to

learn

Measurement model Interaction

The scale “Interaction” is based onVan Wart et al (2020) includes 05 observed variables coded from IR1 to IR5

IR1 Stress-free online learning environment

Van Wart et al (2020)

IR2 I am less afraid to communicate with teachers

IR3 I ask more questions via the comment function

IR4 I am more confident when presenting online

IR5 I am more comfortable participating in

discussions when I study online

Measurement model Academic Integrity

The scale “Academic Integrity” is based on Fish, L A., & Snodgrass, C R (2014) includes 05 observed variables coded from AI1 to AI5

AI I think students tend to cheat more when

1 I lose motivation when I study online

2 I get distracted while studying online

3 I get distracted while studying online

4 I often can't keep up with the lectures when I study online

5 I lack motivation to do individual and group exercises

II Perceived usefulness

6 Online learning saves time

7 Online learning saves money

8 Online learning is very flexible

9 Online learning helps me improve my computer skills

10 The recording function helps me review the lecture

III Perceived ease of use

11 It's easy for me to use online learning apps

12 I quickly mastered online learning applications

13 I rarely have technical problems when I study online

14 I rarely lose my internet connection when I study online

IV Instructor

15 Instructor has good technology skills

16 Lecturers often interact with students

17 Instructor is ready to assist students when needed

18 Lecturers design good lesson content

19 Teachers motivate and interest students to learn

V Interaction

20 Stress-free online learning environment

21 I am less afraid to communicate with teachers

22 I ask more questions via the comment function

23 I am more confident when presenting online

24 I am more comfortable participating in discussions when I study online

VI Academic Integrity

25 I think students tend to cheat more when studying online

26 I feel cheating is more common when learning online

27 I helped my classmates cheat when taking an online test

28 I often can't keep up with the lectures when I study online

29 I feel like I have to cheat to be able to compete with others

VII Perception on online learning

30 I think online learning is very convenient

31 I have a positive view of online learning

32 I find online learning very interesting

33 I love learning online

34 I am ready to study online in the future

3.2 Sample description

3.2.1 Sample size

Sekaran & Bougie (2010) describe sample size as the subset of a population required to make sure that there is enough information in order to come up with the result as well as conclusion Kumar et al (2013) explains the term sample size as the total number of subjects in the sample In short, it indicates the number of respondents or observations to

be included in a study

Choosing an accurate sample size is considered to be one of the most important decisions

in the process of developing a thesis Recently, most of the sample sizes are recommended based on the author's own experiences A wide range of recommendations regarding sample size in factor analysis have been made For example, Guilford (1954) states that the sample size should be at least 200 Cattell (1978) proposed the ratio of three

to six subjects per item, with a minimum of 250 Comrey and Lee (1992) provided the following scale of sample size adequacy: 50 – very poor, 100 – poor, 200 – fair, 300 – good, 500 – very good, and 1,000 or more – excellent

A part of the ideal sample resulted from the author's own experiences, there also exist many formulas that take many factors into consideration These factors include the research approach, analytical method, number of variables, time and resources, sample size used for similar studies, as well as the data analysis program

One of the most popular formulas belongs to Hair, Anderson, Tatham and Black (1998),

in which sample size for factor analysis is determined based on two components, which are the minimum level and the number of scales More precisely, the minimum number of

samples is 5 times the total number of observed variables, and the number of scales is included in the proposed model If the model has m scales, n is the number of samples

This thesis used convenient sampling method to collect survey data for the following reasons:

First, this research is exploratory, so the method of non-probability sampling with convenient sampling proved to be the most suitable

Second, for students, time and cost are two issues that need to be considered when conducting the investigation, so the topic chooses this sampling method so that it does not take much time and costs for work study sample

Third, this sampling method helps the researcher more easily reach the investigated object than other sampling methods

However, the results of the convenience sampling method are prone to significant bias/has an extremely high degree of bias, because those who volunteer to take part may

be different from those who choose not to (volunteer bias), and the sample may not be representative of other characteristics/your entire population

3.2.3 Data collection

Data sources include secondary data and primary data

Secondary data is collected from external sources such as books, journals, research articles and internet databases to provide information about theoretical bases, research models, research methods,

Primary data was collected by implementing questionnaire surveys through the help of google forms To formulate the questions for the questionnaire, many theories and research papers were studied throughout The questions by then would be consulted by the instructor to supplement and complete After getting the instructor approval, the questionnaire is completed and online questionnaire design is conducted

The first and foremost reason that was done using survey questionnaires through google form is to comply with the nationwide lock down In addition, questionnaires prove to be

a convenient tool, since it helps the researcher to save time, cost for the survey and it was easier for respondents to complete the questionnaire Furthermore, it ensures the confidentiality of personal information and the response rate to this type of survey is usually very high Because the research object of this topic is "students of the Banking University of Ho Chi Minh City'', the questionnaire was posted on various Facebook groups in order to reach out and collect their answers

Data collected by survey questions were encoded and entered using SPSS 20.0 data analysis software to facilitate data analysis later

3.3 Statistical data analysis technique

3.3.1 Testing the reliability of a scale

To analyze statistical data, the research project uses SPSS 20.0 software to conduct the reliability test of the scale accompanied by other deductive statistics

The research project uses Cronbach's Alpha coefficients and factor analysis to test the reliability of each scale of factors students’ perception toward online learning

The Cronbach's Alpha coefficient is used to test the reliability of the variables that influence the perspective of online learning Signals that do not guarantee reliability are removed prior to the factor analysis

Factor analysis is used in this project to reduce more or less related variables to combine them into fewer factor groups

3.3.2 Correlation coefficient and logit regression analysis

Before performing logit regression, it is necessary to determine the correlation coefficient between the variables in the model through the correlation coefficient matrix calculated

by SPSS 20.0 software to determine the degree of linear association between the toxic variables up and dependent variable in the regression model In addition, the correlation coefficient matrix also helps to detect multi-collinear phenomena between independent variables, thereby removing one or several independent variables that have a strong correlation with other independent variables

3.4 Conclusion

The main objective of this chapter is to introduce the research process of the topic as well

as the methods carried out in the implementation process, with two main parts: research design and statistical data analysis techniques millet The topic has built the official survey scale and questionnaire for the research paper With the objective reason that the topic is only exploratory, the students' limited budget and the convenience in approaching the research object, the topic came to a decision to use non-sampling techniques probability, with convenient sampling by online questionnaire - sent directly through student groups with a link to the questionnaire designed online Next, process and encrypt the collected data to be ready for data analysis using SPSS 20.0 software

In analytical techniques, the topic uses Cronbach's Alpha coefficient to test the reliability

of the scale, factor analysis to reduce more or less related variables into groups with fewer factors Moreover, Anova test shows the relationship between qualitative variables and selection decisions Besides, the topic also determines the correlation coefficient between the variables in the model to determine the degree of linear association between the independent variable and the dependent sea in the model

CHAPTER 4: RESEARCH RESULT 4.1 Descriptive Statistic

Table 3: Descriptive statistic