HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION FACULTY FOR HIGH QUALITY TRAINING COURSE: DATA ANALYSIS INSTRUCTOR: Ph.D Nguyen Khac HieuRESEARCH ON ONLINE LEARNING, E-LEARNING I
INTRODUCTION
Reasons for choosing topics
The year 2021 has been an eventful year with the most horrifying events - the Covid-19 pandemic is still ongoing Having delayed the process of activities to all areas of the world, even students' attendance to school in classes is minimized Online teaching method is applied instead This is a method that has appeared for a long time but has not been invested nor used prevailing in previous years Therefore, when teaching online has been introduced instead of traditional teaching methods, it has brought a lot of feedback from students - who are directly involved in acquiring knowledge through this learning method.
Ho Chi Minh City University of Technology and Education is no exception, the school has deployed and introduced this learning method and applied it urgently to students, in order to solve the difficult problem - the epidemic is impossible to school in front of me As well as to ensure the school's students' learning and continuing on with the pace.
Therefore, this survey aims to collect the most objective opinions from students of Ho Chi Minh City University of Technology and Education, as well as the satisfaction and actual learning results from the online teaching method this is new From that point out the shortcomings and shortcomings of online learning and new directional solutions to improve the quality and conditions of online teaching and learning of Ho Chi Minh City University of Technology and Education Ho Chi Minh City, bringing student satisfaction, helps students have a learning condition and acquires knowledge through this learning in the most comfortable and effective way.
Research objectives
Determine the factors affecting the student satisfaction of Ho Chi Minh City University of Technology and Education with the online instruction, E-Learning Thereby proposal to upgrade and improve the online instruction, E-Learning.
Identify the advantages, quality and influence of the Ho Chi Minh City University of Technology and Education’s online learning system.
Determine the importance and level of students' absorption of knowledge through online learning methods Give some ideas, comments to improve the online learning system and teaching efficiency on the online platform and bring satisfaction to students of Ho Chi MinhCity University of Technology and Education about e-learning methods.
Research methods and data
Quantitative research is done mainly by methods of online survey and mobile survey. The author relied on the results from studies with similar content to summarize the variables that affect student satisfaction when participating in online learning Then put into a survey to get the opinion of students of Ho Chi Minh University of Technology and Education to synthesize the level of factors that create student satisfaction when studying online, and then propose solutions to improve and improve the quality of online teaching for students. Quantitative research is conducted as follows:
The author will design a survey based on variables of the same studies and the newly added variables.
The author put the link to participate in the survey on the student group of Ho Chi Minh City University of Technology and Education for students to participate in the survey to get opinions.
The data collection process is done according to a convenient method, the author performed in parallel during the study:
- Select students who are currently studying at Ho Chi Minh City University of Technology and Education.
- Send the survey to the subjects via the online form, link the survey form to the online discussion groups of students at Ho Chi Minh City University of Technology and Education on the social networking site Facebook.
- Recieve the survey via online survey, survey participants will be anonymous.
- The author summarizes the survey questionnaires before conducting data entry and analysis.
Meanings of the research
Helping the Ho Chi Minh City University of Technology and Education to offer suggestions for improving the complete online learning system to effectively absorb students' knowledge, and propose measures to help students learn more effectively through the online learning system.
Structure of reports
Chapter 2: Literature reviews and research model
Chapter 5: Conclusions and suggestions, implications of research for Ho Chi Minh City University of Technology and Education to improve online teaching methods.
Definitions of key concepts
Online learning or E-learning is a type of education and learning that uses the Internet as a medium Lecturers and students can use the E-learning system on PCs, tablets, or cellphones with an Internet connection to learn and train Teachers can use the E-learning platform to directly instruct students or send and save lectures and lesson data, including images, videos, and audio, on the system Students can also see many lectures online or in person, interact with other teachers and students, establish discussion topics in the forum, take tests, and much more.
1.6.2 Student satisfaction with training activities concept:
There have been many conceptual studies on customer satisfaction, but these concepts are abstract and hazy because customer satisfaction is defined as meeting the requirements and wants of the consumer Researchers have proposed the following consumer satisfaction concepts:
The "Perception – Perception" hypothesis, which is used to examine consumer satisfaction with the quality of an agency's services or products, is a prominent theory for measuring customer satisfaction That idea is made up of two sub-processes that have independent effects on customer satisfaction: pre-purchase service expectations and post-purchase service perception The total assessment of students' perceived value of training quality is based on their impression of what they gain and lose[ CITATION Trầ19 \l 1033 ] Student satisfaction with training activities is defined as the students' satisfaction with their training and working environment (training programs, textbooks, learning materials, training organizations, staffs, etc.) lecturers, facilities, supporting equipment, support services, etc.) to meet the demand to become a skilled trainer [ CITATION LêT14 \l 1033 ]
There are a lot of different viewpoints when it comes to educational services Many individuals believe that education is a government-provided service Private educational institutions, in fact, provide educational services Higher education services are progressively becoming a known concept for many individuals in Vietnam nowadays. However, there are numerous points of view and disputes regarding higher education services in non-public colleges Higher education is a service, a marketable commodity that is both collective (decided by the state and the general public) and marketable (determined by the market) In some elements, such as competition and monopoly, higher education is still subject to state involvement to some level For a variety of reasons, higher education must be managed by the government Most crucially, the key human resource for socioeconomic development is the result of higher education However, some modern viewpoints hold that higher education services lack sufficient features to be termed a pure public benefit and instead possess several significant features of a private good Customers in higher education are more aware of their wants than service suppliers That is the ideal environment for the market mechanism in higher education to flourish.
1.6.4 The relationship between university service quality and student satisfaction:
Education is one of the service sectors, and it is regarded as the backbone, contributing significantly to a country's development and economy Universities in the country, as well as around the world, have a problem in terms of education quality (Ha Nam Khanh Giao,
2018) “Students, on the other hand, are a university's "clients" (Huang, 2009).
“Service is one of the major components that enhance positive values and can affect the performance of a university,” Berry (1995) remarked The perception of student happiness as a critical instrument for improving university service quality “Higher education can be considered as a “pure service,” suggesting that it has all the distinct qualities of a service,” according to Oldfield & Baron (2000) As a result, “it is critical to attempt to analyze the degree of service quality and to understand the numerous aspects that affect total service quality, which is a factor for educational institutions to design their services in the best possible way,” according to the report (Firdaus, 2006).
LITERATURE REVIEWS AND RESEARCH MODEL
Literature reviews
E-learning is a type of service in which students will be involved in the service delivery process In the field of human-machine interaction, the experience through the interactions affects their satisfaction And after the experience process, the user will evaluate the difference between the original desire and the reality received and this difference is aggregated to create their satisfaction or dissatisfaction about the information system Therefore, the process of assessing user satisfaction must be carried out before and after exposure to the system In which there are some famous models such as a model of End-users computing statisfaction by Doll and Torkzadeh (1988).
Picture 2.1 Research Model “The Measurement of End-User Computing Satisfaction”
Picture 2.2 Research Model “The Measurement of End-User Computing Satisfaction”
In a traditional data processing environment (Figure 2.1), the user interacts with the computer indirectly through an analyst/programmer (Analyst/Programmer) or through operations (Operations) Periodic reports may be required from activities For ad hoc or non- recurring requests an analyst/programmer helps the user In this environment, users may not be aware of specific programs that are run to generate reports In an end-user computing environment (Figure 2.2), decision makers interact directly with application software to enter information or prepare output reports The environment includes a database, a base model, and an interactive software system that allows the user to interact directly with the computer system Figures 2.1 and 2.2 do not depict all the differences between traditional and end-user computing environments Other differences such as software, hardware, support requirements, and control procedures are not illustrated Instead, the purpose of these figures is to illustrate that, in an end-user computing environment, analysts/programmers and operations staff are not directly involved in supporting users, more responsible users for their own application.
The model is applied to perform measurement scales for customer satisfaction for online programs such as a research model on student satisfaction for e-learning
Picture 2.1 Research Model “Investigation and integration of critical success factors for e-Learning learner satisfaction: A case of University of Economics and Law”
Source: Vu Thuy Hang and Nguyen Minh Tuan (2013)
Relevant research overview
(1) Vu Thuy Hang and Nguyen Manh Tuan (2013) researched “ Investigation and integration of critical success factors for e-Learning learner satisfaction: A case of University of Economics and Law”.
The research is mainly aimed at surveying the success factors of e-Learning from the student's perspective In this study, the author chose the specific case of University of Economics and Law and conducted a survey of 40 students and alumni majoring in Management Information Systems on a series of ERP related subjects through analyzed using the Fuzzy AHP tool.
The result is ease of use, enthusiastic faculty with students, and up-to-date learning resources that make the most sense These characteristics are installed into e-Learning and then student satisfaction is re-evaluated Essential factors for success in e-Learning implementation are confirmed But the author also asserts that the factors affecting the success of e-Learning are necessary but not sufficient conditions for the success of that system in particular and of the information system in general
(2) Bui Kien Trung (2016) researched “The relationship between training service quality and student satisfaction and loyalty in E-Learning distance learning”.
Research on the relationship between training service quality and student satisfaction and loyalty in DTTX-E is a highlight in the development of distance learning programs in Vietnam Components of evaluating training service quality have a positive influence on satisfaction and an indirect influence on student loyalty in TTX-E Student satisfaction is the satisfaction with the services provided by the program, student loyalty is measured through attitudinal loyalty (Perception-Emotion-Action).
The results of the study have shown that “training service quality is assessed by three main factors: quality of online information technology system, quality of teaching staff and quality of support services positive relationship with satisfaction and loyalty of students in TTX-E.
Picture 2.3 Research Model “The relationship between training service quality and student satisfaction and loyalty in E-Learning distance learning”
(1) Rebecca A Croxto (2014) researched “The Role of Interactivity in Student Satisfaction and Persistence in Online Learning.”
Online course enrollment is quickly growing, while attrition rates are still significant The effect of interactivity in student happiness and persistence in online learning is examined in this paper The empirical literature was analyzed via the lenses of Bandura's social cognition theory, Anderson's interaction equivalency theorem, and Tinto's social integration theory reviewed empirical literature
Findings suggest that interactivity is an essential component of satisfaction and persistence for online learners and that preferences for online interactivity vary according to the type of student Student–instructor contact was also identified as a critical factor in student satisfaction and persistence among online students.
Picture 2.4 Research model of “The Role of Interactivity in Student Satisfaction and
(2) Michele T Cole, Daniel J Shelley, and Louis B Swartz (2014) Researched “Online Instruction, E-Learning, and Student Satisfaction: A Three Year Study.”
This study was conducted over 3 years, on the satisfaction of graduate students and undergraduate students with online teaching at a university Five hundred and fifty-three students participated in the study Responses were consistent throughout, although some differences were noted in student satisfaction with their experience There were no statistically significant differences in satisfaction levels based on gender, age or education level.
Despite the skepticism that online learning has been proven to be effective and at the same time save money (Bowen, 2013), online education seems to be here to stay To date, there have been many studies on student satisfaction and student learning There seems to be a consensus that both online and ambient tutorials are effective (Wagner, Garippo, & Lovaas,
2011) In Callaway's study (2012), researchers found, positive interactions, with instructors and with other students seem to go hand in hand with student satisfaction Blended instruction is a way to foster interaction while providing convenience and the ability to learn at one's own pace.
(3) Lana C Jackson, Ph.D., Stephanie J Jones, Ed.D., Roy C Rodriguez, Ph.D (2010) researched “Faculty actions that result in student satisfaction in Online courses.”
This study identified faculty actions that positively influence student satisfaction in the online classroom at the community college level Data were collected from student reviews of two web-based courses at two Texas community colleges.
The results of the study indicate that the actions of instructors in online courses seem to impact student satisfaction Identifying the instructor actions that affect student satisfaction in online courses will greatly assist colleges and universities in enhancing their ability to provide an immersive experience quality online for their students.
(4) Cheok, Mei Lick; Wong, Su Luan (2015) researched “Predictors of E-Learning Satisfaction in Teaching and Learning for School Teachers: A Literature Review.”
This study is based on the evaluation of previous studies on satisfaction in using information technology systems From there, building a theoretical model of the factors determining the satisfaction of e-learning in teaching and learning of junior high school teachers There are three main defined groups: user-related characteristics, organizational characteristics, and e-learning system characteristics.
The results show that the teacher's characteristics such as attitude, anxiety and efficiency will to a large extent affect whether the system will operate effectively or not Besides, teachers also need to be supported to change their pedagogy Organization of support in terms of; training, techniques and management, are all important factors needed to motivate teachers to adopt innovation The e-learning system is also very important in ensuring that teachers are satisfied after using it Aspects such as flexibility, interactivity, perceived usefulness, and perceived ease of use must be considered.
(5) Yu-Chun Kuo, Andrew E Walker, Brian R Belland and Kerstin E E Schroder (2013) researched “A Predictive Study of Student Satisfaction in Online Education Programs”
This study has determined that learner-instructor interaction, learner-content interaction, and self-efficacy on the Internet are significant predictors of student satisfaction in online learning process Learner interaction with content is the strongest predictor of any important predictor of student satisfaction In addition, gender and class level significantly influence the interaction between learners and learners.
The results show that the interaction between learners and instructors, interaction between learners with content and their own effectiveness on the Internet are good predictors of student satisfaction while interacting among students and self-regulated learning methods did not contribute to student satisfaction Learner interaction and content explained the biggest difference in student satisfaction In addition, gender, class level, and time spent online per week appear to influence learner-student interaction, Internet effectiveness, and self-regulation.
Table 2.1 Summary of relevant research results.
Chair, Criminal Justice Departm ent Bemidji State Universit y
1500 Birchmo nt Drive NE Bemidji ,
- Natio nal Econ omy Unive rsity
Vu Thuy Hang and Nguy en Man h Tuan (2013 )
The optimal functions support the learning process
Interaction in the learning process
3.The interaction in the learning process
Student’s interaction and self-regulating learning methods
Interaction between learners and content
Research model
Through the process of studying models from previous studies, the author found that there are many research problems about student satisfaction with E-learning They almost only focus heavily on research on certain subjects, but forget about the complementary factors in creating student satisfaction in E-learning There are some factors in the table that are similar to our factor in our model so we decided to group them together and as a result we have 7 factors affecting student satisfaction member for E-learning The author has researched this issue through a survey of students in the University of Ho Chi Minh Technology and Education So our research is one of those worth doing.
Along with that, summarizing the actual situation about online learning system in our university, we propose a research model on the factors affecting the student satisfaction of
Ho Chi Minh City University of Technology and Education with the online instruction, E- Learning including 7 factors: (1) Applications, (2)The investment of the university, (3) Students Satisfaction, (4 ) Teaching methods, (5) System functionality, (6) Interaction in the learning process, (7) Support Services.
Picture 2.6 Proposed Research Model by The Authors
Research Hypotheses
As Online learning is applied in many courses, online learning applications are created and developed to meet the needs of lecturers and students If an app is limited in meeting time will interrupt the teaching and learning process In addition, the application quality is not good, the image is blurred, the sound is not clear is also a factor affecting student satisfaction.
H1: Applications have a positive relationship with satisfaction.
The investment of the university
Interaction in the learning process
Teaching methods had both a positive and negative influence on students' performance Because it is a distance knowledge transmission method, lecturers need to create a motivation for students to develop self-study through teaching methods Teaching methods are also aimed at ensuring the effectiveness of online learning.
H2: Teaching methods have a positive relationship with satisfaction
A full system function of utilities to meet the needs when participating in learning and participating in lectures of teachers, not preventing students from speaking and discussing while studying, affecting student satisfaction with e-learning.
H3: Systems functionality have a positive relationship with student satisfaction.
Immediately support students when students have problems in the process of online learning leading to student satisfaction.
H4: Support services haves a positive relationship with satisfaction.
2.4.5 Interaction in the learning process:
Interaction in the learning process is one of the essential elements for students to be effective in learning From there, it leads to excitement and satisfaction to continue with this learning process.
H5: Interaction in the learning process.
Student opinions are very important to create an online learning system that meets student satisfaction
2.4.7 The investment of the university:
The school's investment in students' online learning is very important, creating conditions for students to study better with online learning methods.
H7: The investment of the university for E-learning systerm.
RESEARCH METHODS
Research process
In order to study the satisfaction of students studying at Ho Chi Minh City University of Technology and Education when studying online, the author has verified the research problem and objectives, based on that, relevant research first, then proceeds to collect data by designing the survey and running the analysis results as well as proposing implications to improve the quality of training by online learning method at school Ho Chi Minh City University of Technology and Education.
Scale construction
1 Applications AP1 High security mode New addition
2 AP2 Students have a discussion environment with other students and the faculty
3 AP3 Lecture design on the app easy to access page
Vu Thuy Hang and Nguyen Manh Tuan (2013)
4 AP4 Lecturers and students easily track the learning process
Vu Thuy Hang and Nguyen Manh Tuan (2013)
TM1 Teachers are enthusiastic and friendly to students
Vu Thuy Hang and Nguyen Manh Tuan (2013)
6 TM2 Content includes many pictures, videos
Vu Thuy Hang and Nguyen Manh Tuan (2013)
7 TM3 The teacher provides full learning materials
Vu Thuy Hang andNguyen Manh Tuan (2013)
8 TM4 Resources are constantly updated from instructors
Vu Thuy Hang and Nguyen Manh Tuan (2013)
SF1 Stable transmission New addition
10 SF2 Teachers use a variety of teaching methods
Vu Thuy Hang and Nguyen Manh Tuan (2013)
11 SF3 There are function buttons to assist students with speaking and interacting with lecture
12 SF4 Students can easily find materials that match their needs
Vu Thuy Hang and Nguyen Manh Tuan (2013)
13 SF5 Resources are designed in many diverse forms (videos, games, )
Lana C Jackson, Ph.D., Stephanie J Jones, Ed.D., Roy C Rodriguez, Ph.D (2010)
14 Interaction in the learning process factors
ITF1 The instructors are dedicated to the students
Vu Thuy Hang and Nguyen Manh Tuan (2013)
15 ITF2 Time to start the class is on schedule New addition
16 ITF3 Create conditions for maximum development for students
17 ITF4 Professional teaching process New addition
SSF1 Support teachers to solve arising related problems
Vu Thuy Hang and Nguyen Manh Tuan (2013)
19 SSF2 Teachers always listen to and understand the needs and wants of the students
20 SSF3 Create favorable conditions in e- learning
Always get support from instructors
Yu-Chun Kuo, Andrew E. Walker, Brian R Belland and Kerstin E E Schroder (2013)
SS1 Students are motivated and ready to learn on e- Learning
Vu Thuy Hang and Nguyen Manh Tuan (2013)
23 SS2 Students are satisfied with the quality of teaching
Vu Thuy Hang and Nguyen Manh Tuan
24 SS3 Students are satisfied with the convenience of e- learning
25 SS4 Infrastructure is used effectively, supporting fast access to students
Vu Thuy Hang and Nguyen Manh Tuan (2013)
26 SS5 Online learning still feels like a traditional classroom to students
Vu Thuy Hang and Nguyen Manh Tuan (2013)
The university is New addition investment of the university equipped with free wifi service for students and wifi system with stable connection
28 INV2 Organized Talk show, Livestream on Facebook to advise and support students to have more effective learning methods
29 INV3 The university has public computers for students to learning online
30 INV4 The university has a student office to contact if you have any questions about E-learning
Data collection and sample
The author decided to choose an online survey, the survey was given internally at Ho Chi Minh University of Technology and Education The survey obtained 103 forms, of which 103 were valid After the analysis, all factors are in accordance with the standard, the cronbach alpha index is above 0.6.
Data analysis (Theory)
Descriptive statistics are coefficients that succinctly describe or summarize a given data set, which may be representative of the whole or a sample of a population.
Descriptive statistics are divided into concentration trend measures and volatility measures Measures of propensity focus on mean, median, and mode, while volatility measures include standard deviation, variance, min and max, kurtosis, and skewness. 3.4.2 Cronbach's Alpha analysis:
Cronbach's Alpha test is a test to analyze and evaluate the reliability of the scale The correlation coefficient of the total variable is the coefficient for the variable the degree of
"linkage" between one observed variable in the factor and the other variables It reflects the degree of contribution to the conceptual value of the factor of a particular observed variable. According to Nunnally & Bernstein (1994), a measure variable has a variable-total correlation (Corrected item-total correlation) ≥ 0.30 then the variable meets the requirements; The scale with Cronbach alpha ≥ 0.60 is an acceptable scale in terms of reliability out of the evaluation factor.
Standards in testing the reliability of Cronbach's Alpha scale:
- If a measurement variable has the correlation coefficient of the total Corrected Item - Total Correlation variable ≥ 0.3, then the variable meets the requirements (Source: Nunnally, J (1978), Psychometric Theory, New York, McGraw-Hill).
- Level of Cronbach’s Alpha coefficient value:
+ From 0.8 to close to 1: the scale is very good.
+ From 0.7 to close to 0.8: good usable scale.
+ From 0.6 and up: qualifying scale.
When performing Cronbach's Alpha analysis for a factor, if the Cronbach's Alpha coefficient of the group is less than 0.6 and no variable in the group has Cronbach's Alpha if Item Deleted is greater than 0.6, then it is necessary to consider removing the whole factor. – In case the value of Cronbach's Alpha if Item Deleted a variable is not too much larger than the group's Cronbach Alpha coefficient (difference is less than 0.1) but Corrected Item
- Total Correlation that variable is greater than 0.3, consider keeping that variable 3.4.3 Exploratory factor analysis (EFA):
Exploratory factor analysis (EFA) is a quantitative analysis method used to reduce a set of many interdependent measurable variables into a smaller set of variables (called factors) so that they are significant but still contains most of the information content of the original set of variables (Hair et al 2009) Instead of studying 20 minor characteristics of an object, you could study only 4 major features, and for each of these major features there are 5 small features that are correlated with each other This saves a lot of time and money in the research process.
Criteria in the EFA analysis:
- Factor loading: Defined as the factor weight, this value represents the correlation relationship between the observed variable and the factor The higher the factor loading coefficient, the greater the correlation between that observed variable and the factor and vice versa.
- Bartlett test has statistical significance (Sig < 0.05): Bartlett test is a statistical quantity used to examine the hypothesis that variables are not correlated in the population In the case of this test has statistical significance (Sig < 0.05), the observed variables are correlated with each other in the population.
- KMO coefficient (Kaiser-Meyer-Olkin) is an index used to consider the suitability of factor analysis The value of KMO must reach the value 0.5 or higher (0.5 ≤ KMO ≤ 1) is a sufficient condition to factor analysis is appropriate.
- Percentage of variance (>50%): It represents the percentage variation of the observed variables That is, considering the variation is 100%, this value shows how much the factor analysis explains.
- Eigenvalue is a commonly used criterion to determine the number of factors in EFA analysis With this criterion, only factors with Eigenvalue ≥ 1 are kept in the analytical model.
Regression analysis is a statistical technique used to estimate the equation that best fits the observed outcome sets of dependent and independent variables.
Regression analysis allows to obtain the best estimate of the true relationship between the variables From this estimable equation, one can predict the dependent variable (unknown) based on the given value of the independent variable (known).
- The t-test method is used to test whether or not the mean of a single variable is different from a particular value, with the initial assumption that the mean to be tested is equal to with a certain number This t-test method is used for distance or scale scale distortion We will discard the original hypothesis when testing our Sig index less than the confidence
26 level (0.05) In statistics, there are three common types of t-tests: One-Sample T Test, Independent Samples T Test, and Pair sample T test.
- Analysis of Variance (ANOVA) is an analytical tool used in statistics that disaggregate aggregate observed variability found within a data set divided into two parts: systematic factors and factors random This ANOVA test technique was developed by Ronald Fisher in 1918.
- The difference between t-test and ANOVA is that T test can only test for variables with two groups of observations, while anova can test for variables with two or more observed variables.
RESEARCH RESULTS
Descriptive statistics
Valid Frequency Percent Valid Percent Cumulative Percent
Valid Frequency Percent Valid Percent Cumulative Percent
N Minimum Maximum Mean Std Deviation Variance
Cronbach’s alpha analysis
4.2.1 APP (Non of Applications Factors):
Cronbach’s Alpha of APP is 0.886.
Item-Total Statistics Scale Mean if
Scale Variance if Item Deleted
4.2.2 TM (Non of Teaching Methods Factors):
Cronbach’s Alpha of TM is 0.924.
Item-Total Statistics Scale Mean if
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
4.4.3 SF (Non of System Functionality Factors):
Cronbach’s Alpha of SF is 0.834.
Item-Total Statistics Scale Mean if
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
4.4.4 SUPF (Non of Support Factors):
Cronbach’s Alpha of SUPF is 0.934.
Item-Total Statistics Scale Mean if
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted SUPF
4.4.5 ITF (Non of Interaction in the learning process Factors):
Cronbach’s Alpha of ITF is 0.899.
Item-Total Statistics Scale Mean if
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
4.4.6 SS (Non of Student Satisfaction):
Cronbach’s Alpha of SS is 0.913.
Item-Total Statistics Scale Mean if
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
4.4.7 INV (Non of The investment of your university):
Cronbach’s Alpha of INV is 0.823.
Item-Total Statistics Scale Mean if Item
Scale Variance if Item Deleted
Cronbach's Alpha if Item Deleted
Exploratory factor analysis (EFA)
Total Variance Explained Compone nt
Initial Eigenvalues Extraction Sums of Squared
Extraction Method: Principal Component Analysis.
Regression
Coefficients a Model Unstandardized Coefficients Standardized
T-test, ANOVA
Independent Samples Test Levene's Test for
Equality of Variances t-test for Equality of Means
CONCLUSIONS AND SUGGESTIONS
Conclusions
The study assisted the authors in developing and validating a model of factors affecting UTE students' satisfaction with online learning, which includes seven criteria related to: (1)Applications, (2) Teaching methods, (3) Systems functionality, (4) Support services, (5) Interaction in the learning process, (6) The investment of the university The findings of the study reveal that there are no differences in UTE student satisfaction based on individual factors such as gender, field of study, training faculty, and so on.
Suggestions
The school must purchase a pro edition of the online learning tool used to teach students Assist students and teachers in making the most of the application's learning and teaching features, with no restrictions on teaching time Furthermore, applications should have more features to make it easier for teachers and students to communicate with one another during lesson tracking.
More quality PowerPoint slides with easy-to-understand explanations should be prepared by teachers so that students can absorb information more quickly To make the lecture content richer and more accessible to students, new sources of documents, photographs, and videos are required Increase interaction between students and teachers more Furthermore, teachers should re-post their lectures so that students who need to hear them again can do so after the lesson.
The school's online website system needs to be improved in terms of quality A lot of students can't log in, and it's easy to get kicked out of the page while taking the test The school needs to consider the transmission of the online site Should upgrade the online site to avoid server overload.
Need more support team for students about learning problems or about the school's online website, in order to fix problems and answer questions for students as quickly as possible speed up the solving of problems that students encounter when using the online site, promptly fix problems when receiving feedback, avoiding the situation affecting the learning process of students.
5.2.5 About interaction in the learning process:
Teachers should pay attention to whether students have understood the lesson or not, giving small exercises in the teaching process to check students' understanding There are questions during online learning so that students can also speak Increase the level of concentration for students in the lesson
5.2.6 About the investment of the university:
Schools should pay more attention to students' online learning Invest in wifi system and computer system to support students to study online at school to the maximum Quickly solve problems that students encounter during online learning Organize seminars or livestreams with content to guide students on online learning methods, helping students learn more effectively.
Limitations
Despite the fact that the report met all of the research objectives, there are several limitations to be aware of: Because of the student of the university of technology and education's random sampling procedure, the results are representative are not exactly There are a number of other elements that affect student satisfaction that is not listed in the report Some pupils did not pay attention and answered to the survey in a superficial way Based on the aforementioned restrictions, the authors offer the following study directions for future studies: Extend the scope of the investigation To improve the explanatory power of the current model, consider adding new factors to it.