EXPLORING CHINESE FACULTY PERCEPTIONS OF
QUALITY STANDARDS FOR ONLINE EDUCATION
By
Xiao Dai

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
High, Adult, and Lifelong Education - Doctor of Philosophy
2014

ABSTRACT
EXPLORING CHINESE FACULTY PERCEPTIONS OF
QUALITY STANDARDS FOR ONLINE EDUCATION
By
Xiao Dai
Online education has become a major component of higher education. With the rapid
growth of online learning, stakeholders are concerned that higher education institutions might
have increased access but lowered quality. Facing scrutiny and accountability demands, online
education programs are often called to demonstrate quality. The challenge facing online
education is how to widen access and reduce costs, while at the same time improving and
ensuring quality.
In order to ensure quality, many organizations in the United States have developed
standards and guidelines that detail the essential criteria for online programs to plan, evaluate,
and improve quality. However, defining and implementing quality standards are complex issues.
There are questions about the completeness of these quality standards. There are also unknowns
of how much these quality standards, as developed by U.S. organizations, can be applied in
educational settings other than the U.S.
This study explored how U.S. quality indicators for online education are perceived by
Chinese faculty. Nine sources from the U.S. literature were identified to represent U.S. online
education quality standards. Thirty-one quality indicators were assembled, and a survey was
administered to 400 Chinese online faculty and their teaching assistants at a Chinese institution.
The results indicate that U.S. quality indicators for online education are perceived by
Chinese faculty as relevant, with high ratings on the perceived importance of these indicators.
Most respondents feel that this set of quality indicators reflects their criteria of quality; and that

China should adopt them. The study also reveals, from the open-ended questions responses, that
U.S. quality standards are not fully capturing the essence of quality for online education. Quality
indicators, as reflected in the U.S. quality standards, focus more on the elements and conditions
that are considered as inputs, but not enough on the outcomes. This suggests that even if the
Chinese institutions replicate these indicators, the quality assurance process is not necessarily
going to address the concerns that come out of the open-ended questions responses.
Because of the shortcomings and the incompleteness of these U.S. quality indicators,
Chinese higher education institutions should be cautious in borrowing them. In order to
successfully apply these U.S. indicators in China, Chinese scholars and institutions should
expand and modify these U.S. quality indicators to suit the Chinese educational environment.
This dissertation further discusses what expansion and modifications are needed, and how China
may go about such modification. The dissertation concludes with the study’s implications for
quality assurance practices in China and for future studies of the quality of online education.

Copyright by
XIAO DAO
2014

ACKNOWLEDGEMENTS

Going through a doctoral program is a journey full of challenges, stress, and joy. I have
been very fortunate to walk this journey together with many individuals, to whom I own sincere
thanks. These individuals include faculty, staff, students, friends, and family members. They
walked this journey with me, with their time, and support. Many conversations happened
between me and these supporters, and each time I gained insights and walked away with ideas or
tips that helped me during this process. Their encouragement, suggestions, discussions, and
intellectual challenges helped me to become who I am today. For their time, thoughts, ideas, and
encouragement, I say thank you.
Finishing the doctoral program is a process with many parts. One of the first was taking
courses. I want to thank faculty members at the College of Education for teaching the courses
and providing me in-depth knowledge of higher education and educational research. I also want
to give thanks to HALE staff members Karla Bellingar, Cathy Ogar, and Kathy Dimoff who
have supported me numerous times.
I want to give special thanks to my graduate study guidance committee and my
dissertation committee members: Dr. James Fairweather, Dr. Punya Mishra, Dr. Marilyn Amey,
and Dr. John Dirkx. Without your guidance, I would probably still be collecting data, or
wondering about what data to collect.
Dr. Punya Mishra, we both remember the first conversation we had about my dissertation.
The conversation was about online education. Even though there were several topic changes, the
final dissertation topic was the one we discussed the first time. Thank you for all the intellectual
conversation, and challenging questions you purposely raised.

v

Dr. James Fairweather, I am fortunate to have the opportunities to take classes from you
and have you as a committee member. Your guidance and suggestions in research design and
data analysis have been essential to this dissertation. Thanks for pointing me to the other
research works in the field.
Dr. Marilyn Amey, thank you for the constant encouragement and advices. I want to
thank you for your support as the chairperson of the department and your suggestions and
comments for my research ideas, the research proposal, and the dissertation. Your suggestions
made my research stronger.
There is one individual who is very special during this journey, my advisor, and
dissertation director Dr. John Dirkx. You have been a tremendous mentor and advisor. Your
teaching and advising was not limited to the classrooms and the office. You often found the
teachable moments to expand my thinking, through traveling and attending conferences. Thank
you for being patient with me.
I also want to thank the administrators and faculty at China Southwest University Online
and Continuing Education College, for their willingness to participant in this study. Their desire
to achieve excellence in online education and become a world class institution was essential to
the success of this project. Thanks to Dean Liu, Dr. Zhimin Tan, Mr. Caiqiang Li and other
faculty and staff, for you participation, suggestion, and feedback.
There are several people whom I want to give special thanks for their contribution to my
dissertation work. Without their suggestions and conversations, this dissertation would be
impossible. I want to thank Dr. Harold Johnson for the intellectual conversations that generated
great research ideas. I want to thank Dr. Richard Houang for numerous intellectual discussions
regarding the research methods and data analysis. You encouraged me to go beyond the obvious

vi

to interpret the deeper meaning of statistical findings. Dr. Doug Campbell, you have provided
great help and support through the College’s Office of Student Writing Assistance. Your
suggestions and comments helped improving the quality of the dissertation. For the many hours
you spent reading my paper, thank you.
There are also other scholars who have helped me in various ways. Some of them are the
experts of online education who provided valuable insights of verifying quality indicators, to
represent the quality assurance practice in the U.S. Dr. Rui Hui Ni, Dr. Leigh Wolf, Dr. John
Bell, Dr. Robin Dickson, and Dr. Todd Marshall, thank you. Additionally, Ms. Wendy Liu
provided reverse translation for the questionnaire, Dr. Amita Chudgar reviewed the survey, and
Dr. Kang Li helped with survey translation and coding verification; three Chinese educators
worked as pilot testers to ensure the survey quality. Mr. Yunfeng Huang, Dr. Wei Han, and
Dr. Jinhua Li, thank you.
Working full time and trying to finish a dissertation is a challenge. I want to express my
thanks to my colleagues for their understanding and support during this journey. I also want to
thank Dr. Sam Larson and Gail Nutter for your support.
In addition, there is a group of friends who supported and encouraged me during this
journey. It would be impossible for me to list all individual’s names and describe how he/she
helped me. Below is an effort to list a portion of the individuals to represent such supporting
group, and you each know your support goes beyond this debriefing here. These individuals are:
Dr. Cass Book, Dr. Kang Li, Ms. Stacy Clause, Dr. Susan Hutchison, and Dr. Caleb Chan. This
group also includes members from my HALE cohort at Michigan State University, and other
HALE students.

vii

Thanks also go to College of Education Graduate Student Record Office Dr. Michael
Sedlak, Jodi Potter, Joni Burns; Student Affair Office Jackie Hohenstein, Dr. Susan Dalebout;
MSU Educational Assistance Office MS. Julie Rorick; MSU IRB office Ms. Tonya Jamison and
Steven Smith; and MSU Writing Center staff.
Lastly, I want to thank my family. I want to especially thank my wife Carla and my
daughter Jennifer for their support and sacrifices they have made over the past six years. My
thanks also go to my nephew Hongbo Dai and my parents for their support for the completion of
the project.

viii

TABLE OF CONTENTS

LIST OF TABLES ……………………………………………..……………..………….. xii
LIST OF FIGURES ………………………………………………….……….….……….. xiii
CHAPTER 1
Introduction ………………………………………………………………….…..……….. 1
1.1 Statement of the Problem ……………………………………..……..……….. 3
The Issue of Quality ……………………………………………………… 4
Online Education Expansion in China ………………………………….... 5
Online Education Quality Assurance in China …………………………... 6
Quality Standards from the United States ………………………….…..… 7
The Issue of Applicability …………………………………………...…… 8
1.2 Purpose of the Study ………………………………………………………….. 9
Research Questions ……………………………………………………….. 9
1.3 Significance …………….…………………………………………….…....….. 12
CHAPTER 2
Literature Review …………………………………………………………………...…….. 14
2.1 Quality Standards for Online Education: Theoretical Perspectives …….…….. 14
Quality Definition – Multiple Dimensions ……………………………….. 15
The Debate on Educational Quality Standards ……………..……….……. 16
2.2 Quality Standards for Online Education in the U.S. .…………..……….…….. 19
Quality Standards Developed by U.S. Organizations ………….…………. 20
Online Education Quality Standards Research …………………………… 21
Quality Indicators and Categorization ……………………………………. 23
2.1 Online Education in China ………….…………………………….…………... 24
The Current State ………………………….……………………………… 24
Online Education Quality Assurance in China ….…………………….…. 25
Why Should China Care about the U.S. Quality Standards? ……………... 26
2.4 Chapter Summary ……………………………………………………………... 27

CHAPTER 3
Methodology ………………………………………………………………………………
3.1 Introduction ……………………………………………………………………
Research Questions ……………………………………………………….
3.2 Context ………………………………………………………………………...
3.3 Participants ……….………………………………………………….………..
3.4 Survey Development …..………………………..…………………………….
Prior Study …………………………………………………..……………
U.S. Quality Standards Selection ………………………………………….
Expert Review …………………………………………………………….

ix

29
29
30
31
32
33
33
34
36

Survey Rating Questions …………………………………………………. 39
Open-ended Questions ……………………………..…………………….. 40
Demographics ………………………………………………….…………. 43
Translation and Reverse Translation ……………………………………… 43
Pilot Survey ………………………………………….…………………… 43
IRB Approval ………………………………………..…………………… 44
3.5 Data Collection ………………………………………………………..……… 45
3.6 Data Analysis ……………………………………………………….………… 45
Data Cleaning and Preparation …………………………………..……….. 45
Statistical Analysis ……………………………………….….…….……… 46
Open-ended Question Analysis …………………………………………… 47
Post Hoc Analysis ……………………………………………….………... 47
Bias ……………………………………………………………………….. 48
3.7 Chapter Summary ………………………………………………...…………. 49
CHAPTER 4
Presentation of Findings …………………………………………………….….………… 50
4.1 Statistical Analysis ……………………………………………..…………….. 50
Respondents’ Characteristics ………………………………….…..……… 50
Categorization for the Quality Indicators of Online Education …….…..… 51
Categorization Reduction …………..…………………………………….. 53
Quality Indicator Reduction ………………………………………………. 53
Internal Consistency Test …………………………………………………. 54
Description of the Categorization ………………………………………… 55
Ratings for the Importance of the Quality Indicators …….……..…….….. 57
Ratings for the Presence of Quality Indicators …………….………..…… 58
Presence and the Level of Satisfaction …………………..……..… 58
Presence and Concern for Quality ………………………..………. 60
Section Summary …………………………………………………….…… 62
4.2 Open-Ended Questions Analysis and Findings…………………..……………. 63
Data Preparation ………………………………………………………….. 63
Coding Process ……………………………………………………………. 63
How Do Participants View the U.S. Quality Standards? ............................. 64
Findings Organized by Factors .................................................................... 69
Findings Related to Institutional Support ………………………… 69
Findings Related to Faculty Support ……….……………………. 70
Findings Related to Student Support ……………………………… 72
Findings Related to Teaching and Learning ………………………. 73
Findings Related to Assessment ………………………………….. 74
Summary of the Open-Ended Question Findings …………………………. 75
4.3 Post Hoc Analysis ……………………………………………….……………. 76
Management Factor …………………..……………………….………….. 76
Reducing the Number of Factors …………………………………………. 77
4.4 Chapter Summary ………………………………….…………………..……… 78

x

CHAPTER 5
Discussion ………………………………….………………………………………...…… 80
5.1 Summary of the Study ………………………………………………….…….. 80
Are the U.S. Quality Standards Perceived as Relevant …………………… 80
The ratings ……………………………………………………..… 80
The open ended Questions …………………………………..…… 81
Criteria of Quality ……………………………………………..… 81
5.2 Are the U.S. Quality Standard for Online Education Adequate? …….….…... 82
Measuring Quality - Inputs vs. Outcomes ………………..……….……… 83
The Incompleteness of the U.S. Quality Standards ………..……………… 84
What Does this Mean to the U.S.? ……………………………………….. 86
What Does this Mean to China? …………………………………………. 86
5.3 The Modifications Needed to Adopt the U.S. Quality Indicators ……………. 87
Modifications to Expand the Coverage of Indicators ………………..…… 87
Modifications to Provide the Details as Action Plans ………………....…. 89
What Would International Quality Standards Look Like? …………….…. 91
5.4 The Connection between This Study and Other Studies ……………………… 92
The Categorization Empirically Verified ………………………………… 92
Inter-Relationships among Factors ………………………………….……. 93
Further Factor Reduction …………………………………………….…… 94
5.5 Implications ………………………………………………………….………... 96
Implications for Institution Policy ………………………………………… 96
Implications for Faculty Support ……………………………………….… 98
Implications for Student Support …………………………………………. 99
Implications for Teaching and Learning ………………………………….. 100
Implications for Assessment ……………………………………………… 101
Section Summary ……………………………………………………….… 102
5.6 Limitations ……………………………………….……………………………. 102
5.7 Future Study ………………………………………………………….……..… 104
5.8 Conclusion ……………………………………………………………………. 104
APPENDICES ……………………………………………………………………….……. 108
Appendix 1 Expert Review on the Selection of Indicators ……...…………..….… 109
Appendix 2 Reverse Translation Results (Chinese to English) ………….…......… 113
Appendix 3 Cognitive Interviewing Protocol ………………………..…….…..… 116
Appendix 4 English Version Survey........................................................................ 117
Appendix 5 Chinese Version Survey....................................................................... 121
Appendix 6 Seven Factor Categorization Based on U.S. Literature …………...… 125
Appendix 7 Five Factor Categorization for Quality Indicators …………....…..… 127
Appendix 8 Open-ended Questions Analysis Results ………………….……....… 131
Appendix 9 Further Reduce Factors ……………………………….…….….....… 141
Appendix 10 ADDENDUM ……………………………..…..…………………… 142
REFERENCES ………..……………………………………………………….….……… 144

xi

LIST OF TABLES

Table 1. Common Quality Indicators of Online Education ………………………….…… 23
Table 2. Quality Indicators Category Selection ……….…………………………………. 35
Table 3. U.S. Quality Indicators and Their Categorizations ………..……………………. 37
Table 4. Summary of the Respondent Demographics ……………………………………. 51
Table 5. The CFA Results ……..…………………………………………………………. 54
Table 6. The Reliability Test Results for Five Factors ………..…………………………. 54
Table 7. The Distribution of the Perceived Importance .…………………………………. 57
Table 8. The Distribution for Perceived Presence ..………………………………………. 58
Table 9. The Association Between the Five Factors and the Satisfaction .………………. 59
Table 10. The Association Between the Five Factors and the Concern for Quality …....… 61
Table 11. The Summary of the Association ...................................................................…. 62
Table 12. The Reasons for Having Concerns for Quality …….………………………….. 65
Table 13. The Responses for China’s Adoption of the U.S. Quality Indicators …..…….. 67
Table 14. Further Factor Analysis Results …………….………………………………….. 77
Table 15. The Perceived Characteristic for Online Programs with Quality ….………….. 83
Table 16. The Rating Comparison of Perceived Importance …………………………….. 93
Table 17. CFA Results for Seven-Factors ……….………………………………………. 126
Table 18. Five Factors CFA Results………………………………………………….…… 128
Table 19. The Respondents’ Beliefs about Online Education ………………………….… 132
Table 20. The Characteristics of Quality Online Programs ……………….…………….... 133
Table 21. Faculty’s Reason for Satisfaction …………………………….………….……. 134

xii

Table 22. Faculty’s Reason for Dissatisfaction ………………………………….…….…. 134
Table 23. The Respondents’ Reasons for Having Concerns ……………….………...…… 135
Table 24. Reasons for Not Willing to Teach Online Courses …………………………...

136

Table 25. Reasons for Willing to Teach Online Courses ……………………………….

137

Table 26. Suggestions to Improve Quality in China …………………………………….. 138
Table 27. Reasons for the Reflection of Quality Criteria ………………………………… 139
Table 28. China’s Adoption of the U.S. Quality Indicators ……………………………… 140
Table 29. Further Factor Reduction Results …………………………………………...… 141

xiii

LIST OF FIGURES

Figure 1. The Question Example to Ask Online Education Experts ………..……………. 37
Figure 2. The Question Example for the Rating of Indicators ..……………..……………. 40
Figure 3. The Example of Open-ended Questions ………………………...…………….

42

Figure 4. Five Factor Categorization for Quality Indicators ……………..………………. 55
Figure 5. The Association between Faculty Support and Satisfaction ………………...…. 60
Figure 6. The Association between Institutional Support and the Concern for Quality ….. 61
Figure 7. The Answers for Whether the Respondents Had Concern for Quality …..……. 64
Figure 8. The Answers for Whether the Indicators Reflect Criteria for Quality ……..…. 66
Figure 9. The Results for Whether China Should Adopt the U.S. Quality Indicators ……. 67
Figure 10. Additional Coverage of Quality Indicators to Reflect Different Perspectives ... 88
Figure 11. The Implementation Plan for Different Stakeholder Groups …………………. 91
Figure 12. International Quality Standard for Online Education ………………………… 92
Figure 13. Correlations for Five Factors …………………….…………………………… 94
Figure 14. Five Factors Further Divided into Two Groups ………….…………………

95

Figure 15. Seven Factors CFA Results ……………………………………………..…..

125

Figure 16. Five Factors CFA Results ………………………………………………….… 127
Figure 17. The Respondents’ Willingness to Teach Online Courses …………………….. 136
Figure 18. The Reflection of Quality Criteria ………………………………………..…... 139
Figure 19. China’s Adoption of the U.S. Quality Indicators ……………………………... 140
Figure 20. Factor Analysis Scree Plot ………………………………………………….… 141

xiv

CHAPTER 1
Introduction
Online education is becoming ubiquitous and mainstream (Christensen & Eyring, 2011).
Its pervasiveness and popularity have proven its value as an important component of higher
education. Despite the many benefits of online education, the concern about a potential decline
in academic standards has worried stakeholders (Mariasingam & Hanna, 2006; Parker, 2008;
Shelton, 2011). Institutions increasingly feel pressure to improve the quality of online education,
in order to comply with external accountability demands. During this process, more reliable and
effective quality assurance measures are needed (Van Damme, 2002).
Many organizations in the United States have developed standards and guidelines that
detail the essential criteria necessary for online programs to plan, evaluate, and improve their
quality (Moore, 2011). While educators acknowledge the usefulness of these standards in
helping online programs achieve quality, it is important to remember that educational quality has
multiple dimensions. The definition and measurement of quality depends on what the purpose is,
from whose perspective, at what level, and at what time (Mariasingam & Hanna, 2006). With
many variables, quality becomes a contingent concept. It can mean different things to different
people (Shelton, 2011). Understanding of quality and the implementation of quality standards
become critical in order to design and implement quality standards of online education.
The different understandings of quality can cause special problems when quality
standards are used in international settings. There are unknowns of how much these quality
standards, as developed by U.S. organizations, can be applied in educational settings other than
the U.S. at the same time, the Chinese government initiated a major higher education experiment
in 2000, by granting 38 Chinese national universities the right to start online programs (Zhang,

1

2004). After a decade of development, online education has become a major component in
Chinese higher education, with over 12% of all students in the higher education sector (Jung,
Wong, Li, Baigaltugs, & Belawati, 2011). The number of students enrolled has been impressive
for Chinese online education programs. What comes with the expansion and growth of online
education, however, has been concerns about the quality of online education. The Chinese
government and higher education institutions are looking for models that China can use to ensure
quality (MOE, 2010; Zhang, 2004). Since the U.S. is considered a global leader in higher
education, it is logical to ask whether there are parts from the U.S. quality standards that can be
learned from.
This dissertation project explored the extent to which online quality standards developed
by organizations within the United States are perceived as relevant by Chinese online faculty.
The main research questions were whether the U.S. standards for the quality of online education
are relevant to China from Chinese faculty’s perspective; and whether the U.S. quality standards
sufficiently reflect the quality of online education, based on the perspectives from Chinese online
faculty.
Online education in this paper is defined as “planned learning that normally occurs in a
different place from teaching, requiring special course design and instruction techniques,
communication through various technology, and special organizational and administrative
arrangements” (Moore & Kearsley, 2011, p. 2). Throughout this dissertation, the terms “online
education,” “online learning,” “distance education,” and “distance learning” are used
interchangeably to mean the definition of online education presented above. In this chapter, the
opportunities and challenges of online education in China are first presented as background

2

information for the study, followed by the study’s problem statement, its purpose, and the
significance of the study.
1.1 Statement of the Problem
Expanding access to higher education has never been more important (Hanna, 2012).
The challenges that are associated with increasing access to higher education are formidable.
One major barrier has been the high cost that has limited access to higher education (Heller,
2008). To increase higher education access, there needs to be a way to provide quality education
without the high cost. The traditional residential-campus model does not provide much
assistance in this regard (Hanna, 2012). Under the traditional brick-and-mortar residential
campus model, enrolling more students means that more teachers and spaces are proportionally
needed (Twigg, 2013). The current economic situation, however, tells us that the resources are
not available to support this type of expansion. Universities have to go beyond traditional
methods in order to provide access at a more reasonable price (Bramble & Panda, 2008; Taylor,
2009).
At the same time, several critical factors have contributed to the expansion of online
learning: the need for flexible access to learning opportunities, the belief that online learning will
reduce costs and increase productivity (Naidu, 2012), the technology advancement and
theoretical research on using technology in teaching and learning (Mishra & Koehler, 2006), and
improvements in instructors’ attitudes towards online learning and pedagogical competence
(Sener, 2010).
Recognizing the benefits of online education in terms of widened access, flexible
learning schedules, and the possibility of reducing costs, most universities in the U.S. have
implemented some form of online education program (Crotty, 2011). Students enrolled in online

3

programs, as a percentage of the total U.S. post-secondary enrollment, reached 31% in 2011.
Online education is becoming ubiquitous and mainstream (Christensen & Eyring, 2011). Its
pervasiveness and popularity have proven its value as an important component of higher
education. It is anticipated that the increase of online program offerings and in online student
enrollment will continue (Allen & Seaman, 2011).
The Issue of Quality
Despite the many benefits of online education, the concern about a potential decline in
quality has worried stakeholders (Mariasingam & Hanna, 2006; Parker, 2008; Shelton, 2011).
To comply with the external accountability demand, institutions increasingly feel pressure to
improve the quality of online education. More reliable and effective quality assurance measures
are needed (Van Damme, 2002). Understanding the definition of online education quality is the
first step.
What is online education quality? Researchers have suggested that the meaning of online
education quality is dependent on the purpose, level, and perspectives of stakeholders
(Mariasingam & Hanna, 2006). With so many variables, online education quality becomes a
contingent concept. Depending on the region, the needs of society, the type of institution, and
the level of the program, online education quality could have different meanings to different
people. Even though the notions of quality (“excellence,” “standards,” “quality assurance,” and
“benchmarks”) are often conflated in policy documents and in the literature (Vidovich, 2001),
the lack of consistent, agreed upon definitions of quality can be problematic. It is important to
have quality guidelines as a framework to plan, implement, and evaluate online programs. The
search for the clarity of online education quality standards has been the pursuit of many scholars
and organizations (Meryer, 2002, p. 22).

4

Online Education Expansion in China
The U.S. is hardly alone in feeling the pressure to increase higher education access and to
improve quality. China is one international example that is in need of providing more access to
higher education. Chinese higher education had a great expansion over the past two decades. In
1995, China had 1,000 post-secondary institutions that enrolled 5 million college students. By
2010, there were 2,305 Chinese institutions that enrolled 21 million college students (Jung et al.,
2011). But even the additional institutions still cannot meet the Chinese students’ needs,
especially those non-traditional adult students who do not pass the college entry exam and
therefore are shut out of a traditional college education (Jung et al., 2011).
In the year 2000, the Chinese central government initiated an educational experiment by
granting 38 national universities the right to start online education programs. After a decade of
development, online education has become a major component of Chinese higher education, with
over 3 million students enrolled in online programs in 2010 (Jung et al., 2011). The expansion of
online education in China is not without serious challenges. The number and the scale of
enrollment have been impressive for Chinese online education programs, but the remaining
question is about quality.
Three years into the online education experiment, there was growing concern in China
over the quality of these pilot online programs (Wu, 2006). There have been issues and
challenges, including lack of teacher-student interaction, isolated student learning, and
questionable recruiting practices. What happened in China reflects the findings from a recent
survey in the U.S, in which one-third of U.S. academic leaders believe that learning outcomes for
online courses are inferior to those of face-to-face instruction (Richardson, McLeod, & Dikkers,

5

2011). As China expands its online education to increase access to higher education, the quality
of online education has become a significant concern to Chinese online education stakeholders.
Online Education Quality Assurance in China
If the goal for the Chinese government and institutions is to improve the quality of online
education, then quality has to be defined and measured. Because online education is a recent
development in China, there have not been quality standards in China that are comparable to the
standards developed by U.S. organizations (such as SLOAN-C) and accreditation agencies (such
as C-RAC). There is not a regional accreditation agency (such as CHEA), nor an organization
like SLOAN-C, to coordinate and develop comprehensive quality benchmarks. Each Chinese
institution has to experiment and establish quality criteria on its own. The results demonstrate
great variation in quality among these online programs.
While Chinese institutions need quality standards as a framework to plan, implement, and
evaluate online programs, the search for the clarity of online education quality standards has
been the pursuit of many Chinese scholars. Over the past decade, Chinese scholars indeed have
been working on developing quality standards for online education. For example, a technical
standard, based on the ISO9000, was developed to enable meta-data compatibility between
different online education platforms (Zhu, 2001). A national standard for the management
aspects of online education was also in the process of being developed (Guo, 2009). What is
lacking in the Chinese quality standards development are standards with detailed quality
indicators to ensure better online program quality, with the focus on teaching and learning.
Chinese scholars have realized that additional work is needed to develop quality
standards in order to help Chinese online programs become more successful (Guo, Huang, &
Chen, 2009). In order to speed up the process of developing quality standards for online

6

education, Chinese scholars want to learn from other countries that have had more experiences in
this area ( Chen, 2012; Ding, 2005; Pan, 2006; Zhang, 2004). In its 2020 National Educational
Plan, the Chinese government also called on Chinese higher education institutions to improve
their quality and to become better connected to the international academic community (Chinese
MoE, 2010).
One country from the international community that came into the picture is the U.S.,
because of its highly regarded higher education. The Shanghai Jiao Tong University Academic
Ranking of World Universities ranked 17 U.S. institutions among the world's top 20, 35 among
the top 50, and 52 among the top 100 (ARWU, 2013). Because of the good reputation U.S.
higher education has, it is logical for the Chinese to investigate whether there are parts of the U.S.
quality standards that can be learned from.
Quality Standards from the United States
Many online education quality standards have been developed within the U.S. to help
institutions plan and evaluate online programs (Bishop, 2006; CHEA, 2008; Shelton, 2010). For
example, the New England Association of Schools and Colleges Commission on Institutions of
Higher Education (CIHE) has proposed a set of guidelines to evaluate online program quality
(CIHE, 2009), in which eight categories have been presented with guidelines that institutions can
use to measure online program quality. The Council of Regional Accrediting Commissions (CRAC) has also proposed guidelines to evaluate the quality of online learning, in which eight
categories are presented with detailed aspects that institutions can follow (C-RAC, 2009). As a
prominent online education promoter in the U.S., the Sloan Consortium (Sloan-C) has also
proposed a five-pillar framework that includes five basic quality components within online

7

education, namely Learning Effectiveness, Scale, Access, Faculty Satisfaction, and Student
Satisfaction (Moore, 2011).
To validate the quality standards and quality indicators that have appeared in the
literature, the Institute for Higher Education Policy conducted a research project that included six
U.S. institutions (Phipps & Merisotis, 2000). The study examined the applicability of common
quality benchmarks available around the year 2000, and it confirmed that most of the
benchmarks were appropriate and necessary to ensure the quality of online education programs.
The results of the study brought research data to the heated debate about online education quality,
and the results provided a more tangible meaning of quality assurance. The final report, Quality
on the Line, identified twenty-four online program quality benchmarks that were subsequently
adopted by the National Education Association.
The Issue of Applicability
Even though many online quality standards exist, there are still unknowns when applying
these quality standards to practice. One of these unknowns is whether the U.S. quality standards
capture the full aspects of online education quality. Another is to what extent these quality
guidelines, as developed by U.S. organizations, can be applied in educational settings other than
the United States.
Can these U.S. quality standards for online education be applied in China? Are criteria
that are considered important by U.S. scholars perceived the same way by the Chinese? On the
one hand, one could argue that the world is getting closer because of globalization, brought by
the advance of technology. The quality standards developed in the U.S. may well be suited to
other countries. On the other hand, educational quality is contextual and subjective. How do we
know that the U.S. quality indicators capture the essence of the quality of online education in

8

China? While it is not clear specifically how successful the adoption and the implementation of
foreign quality standards will be, there is reason to believe that the process could be problematic
(Schmidt, Houang, & Shakrani, 2009). When the Chinese turn to the international community to
learn about best quality assurance practices, they should do so with full knowledge of whether or
not these foreign quality indicators are appropriate to the Chinese context.
1.2 Purpose of the Study
This dissertation project studied a Chinese University’s online program that purported to
be successful, with proven records of widening access and lowering costs. The researcher
investigated these questions: 1) To what extent were the online quality indicators developed by
organizations within the U.S. perceived as relevant to China by Chinese online faculty? 2) Do
U.S. quality indicators, included in this study, sufficiently capture the essence of quality for
online education from the Chinese faculty perspective? Faculty were the main participants for
the study, because faculty decide course offerings, design course structure, develop course
materials, teach courses, and evaluate students’ learning. The faculty are also the group that has
been facing many challenges: new technology, pedagogy for online teaching, transition from
face-to-face to online teaching, new skills and requirements, and new ways of interacting with
students. The faculty’s perceptions and actions can greatly shape and influence online program
quality. Therefore, the study focused on faculty perspectives.
Research Questions
Do Chinese faculty perceive U.S. quality standards for online education as relevant?
Sub-questions:
1. To what extent do Chinese faculty perceive the importance of U.S. online program
quality indicators?

9

2. To what extent do Chinese faculty perceive the presence of U.S. online program
quality indicators?
3. Is there any association between participants’ satisfaction level for online teaching
experience and the perceived presence of quality indicators?
4. Is there any association between participants’ level of concern for quality and the
perceived presence of quality indicators?
5. Do the U.S. quality indicators included in this study sufficiently capture the essence
of quality for online education?
The study first explored whether Chinese faculty agreed that these quality indicators that
are perceived as important in the U.S. are also important in China. In order for these U.S. quality
indicators to be useful in China, they have to be perceived by Chinese faculty as important.
Second, the degree of relevance is demonstrated in practice. The survey explored to what extent
these benchmarks were being practiced by Chinese faculty. The third sub-question and the
fourth sub-question were added after the initial proposal. These two questions were asked
because they were related to the main research question of whether these indicators were valid to
China. This was a validation for the legitimacy of these U.S. quality indicators. Second, the
researcher wanted to know whether there are additional quality indicators suggested by Chinese
faculty, and what the U.S. can learn from this study to improve its quality standards. The
researcher identified nine major U.S. online quality standards and research publications
developed by scholars and accreditation agencies. By analyzing these standards, 31 online
education quality indicators were assembled, and a survey was designed. The survey was
administered to 400 Chinese online faculty and their teaching assistants at a Chinese institution.

10

The online program at ABC University (pseudonym) started as one of 38 Chinese online
education experiments in 2000. It is a dual mode institution which added online education to its
previously established campus and class-based teaching. The online education curriculum at
ABC University is characterized as teacher-guided and learner-centered, which encourages
autonomous learning (ABC University, 2013a). The online program has demonstrated its
effectiveness in providing access and lowering costs. In the 2012 academic year, ABC
University’s online program enrolled 75,000 students in 37 majors.
Quality has been a main focus at the Online College of ABC University. Various
procedures have been designed and implemented to ensure quality (ABC University, 2013b).
The results from a pilot study, which was conducted in 2012, showed that online education
quality is a major challenge facing Chinese higher education. Maintaining quality was
repeatedly mentioned by Chinese scholars and administrators. According to the faculty and
administrators from ABC University (China), a great amount of resources has been dedicated to
many innovative practices to improve and ensure online learning quality. The researcher began
to wonder about the quality standard of this Chinese online program. Are quality criteria that are
considered important by agencies and scholars in the U.S. perceived the same way in China? If
not, what is different?
ABC University’s online program was selected as the study site because of its substantial
experiences in online education. The ABC University has been taken as the first measure. If
there seems to be evidence that the findings from this study are beneficial to Chinese online
education, more institutions can be added for future study.

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1.3 Significance
By studying how Chinese online faculty members view the U.S. quality indicators, this
study can help Chinese scholars and administrators better understand different policies, opinions,
and quality assurance practices that exist in the U.S. This study can potentially benefit both the
U.S. and Chinese institutions; and it can contribute to the general knowledge of educational
quality in several ways.
First, when Chinese scholars and institutions begin to explore and develop online
education quality standards, it is beneficial to look around and learn from other more developed
practices. However, the many contextual differences that exist between the two countries have
to be taken into consideration. Some practices that are regular and common to U.S. scholars and
practitioners may be difficult to understand by the Chinese, and vice versa. Knowing to what
degree U.S. quality standards are relevant can help Chinese scholars determine whether to adopt
the U.S. standards.
Second, it is also possible for the U.S. to learn from Chinese ways of addressing online
education quality issues. The results from the study can be used to encourage U.S. institutions to
reflect on what can be done differently in order to ensure the quality of online education, as the
U.S. also faces a similar quality issue.
Third, higher education is becoming globalized, thanks to new technologies and new
communication channels (Cunningham et al., 2000). The result of globalization is that there will
be more educational exchanges (staff, students, and programs), more cross-border program
offerings, and more international trade in the form of education services. Many U.S. higher
education institutions will expand their operations to other countries, including China. Many of
them will do so through an online education format. It is important to know how an online

12

program’s quality is perceived, practiced, and measured in other countries, where the educational
environment can be very different from the United States.
Lastly, the study also contributes to the broader conversation on the need for a global
standard of online education quality. The study not only answers how U.S. quality standards are
perceived by Chinese online faculty; it also has value to other international online education
settings. Is the world heading toward a global convergence of online education quality assurance,
regardless of different settings among countries? Or, are the contextual differences so great that
it is impossible to do so? This study can potentially speak to the argument to have common
online education quality benchmarks, or it cannot support the same argument.
The next chapter introduces the literature used to frame the study, and it provides a
synthesis of the current U.S. online education quality benchmarks and indicators, which were
used as the basis for the survey instrument.

13

CHAPTER 2
Literature Review
There are three bodies of literature which pertain to this study. The first is the
phenomenon of online education expansion in China and the concerns for quality that have come
with the expansion. The second includes the definition of quality for online education and its
multidimensional characteristics. The third body of literature details online education quality
standards developed by U.S. organizations. By analyzing these standards, a set of online
education quality indicators were assembled to reflect current U.S. online education quality
assurance practices.
Sources for the literature come from major educational research databases, such as ERIC
(firstSearch) and ERIC (Proquest). Keywords used for the literature search were “online
education,” “quality standard,” “quality assurance,” and “globalization.” Several handbooks in
distance education and documentation from major international organizations were also
referenced.
2.1 Quality Standards for Online Education: Theoretical Perspectives
Despite significant strengths and unprecedented accessibility, there are also weaknesses
associated with online learning, including lack of teacher-student interaction, students studying
in isolation, and uneven enrollment across disciplines (Hardy & Bower, 2004; Saba, 2005). With
the rapid growth of online learning, stakeholders are concerned that higher education institutions
might have increased access but lowered quality (O’Brien, 2012). Facing scrutiny and
accountability demands, online education programs are often called to demonstrate quality
(Parker, 2008; Shelton, 2011). The challenge facing online education is how to widen access and
reduce costs, while at the same time improving and ensuring quality (Jung et al., 2011).

14

What is the quality of online education? How do we know if an online program has
quality? The characteristics of a quality online program include 1) providing clear statements of
educational goals; 2) sustaining the institutional commitment to support learners; 3) engaging in
a collaborative process of discovery, and 4) improving the teaching and learning environment
(Parker, 2008). In practice, however, the meaning of quality for online education is a very
complex issue. It has to cover all aspects of online teaching and learning, which includes
planning, faculty, students, technology, teaching and learning, and evaluation. In this study, the
researcher used a framework to take into consideration the multiple dimensions of educational
quality.
Quality Definition – Multiple Dimensions
There are three dimensions to be considered in order to understand the quality of online
education: the purpose of defining quality, the perspective, and the level of program
(Mariasingam & Hanna, 2006). The first dimension is the purpose of defining quality. Is the
purpose to compare online programs in order to know which program has better quality? Or is
the purpose to improve online program quality? The answer will decide how quality is defined
and measured (Chaney et al., 2009). For example, when the purpose is to rank online programs,
the focus could be on library volumes, faculty rank, instructional methodology, contact hours,
class size, or student grade point average (Oblinger, 1998). When the purpose is to improve
quality, institutional mission statements, evaluation, and improvement plans will be the focus.
Quality is also a construct relative to stakeholders’ perspectives and interpretations
(Cleary, 2001, p. 20). For example, institutional administrators might relate quality more to
accreditation, management, and regulations, while faculty might relate quality to course material
development, for which they need adequate technical and pedagogical help. Therefore, the

15

concerns and focus of administrators can be quite different from those of faculty (Benson, 2003).
Because different stakeholder groups can interpret differently what online education quality
should be (Benson, 2003), any discussion about online education quality must first ask from
what perspective the quality is considered (Twigg, 2001).
The third dimension of quality is the level of the program about which quality is
concerned. For example, at the course level, quality can be measured by learning flexibility,
responsiveness, and teacher-student interaction. At the institution level, however, quality can be
measured by technology, retention rates, persistence rates, and so forth (McGorry, 2003;
Mariasingam & Hanna, 2006). Because of the multiple dimensions, the quality of online
education becomes a complex and contingent concept. A clearer understanding of the concept of
quality and quality standards has always been the pursuit of organizations and scholars.
Depending on the needs of society, the type of institution, and the level of the program,
online education quality could have different meanings to different people. Even though the
notions of quality (“excellence,” “standards,” “quality assurance,” and “benchmarks”) are often
conflated in policy documents and in the literature (Vidovich, 2001), the lack of consistent,
agreed upon definitions of quality can be problematic (Meryer, 2002, p. 22).
The Debate on Educational Quality Standards
How does one ensure that an online program has quality? Some scholars have suggested
that rigorous and uniform academic standards must be defined in order to ensure quality. They
argue that the consequence of not having standards could be a lower quality education (Schmidt
et al., 2009). Many U.S. organizations have published standards for this purpose. Critics of
educational standards, on the other side, claim that the U.S. is on the wrong path in using
standards as the solution to fix educational problems. By strictly adopting government-set

16

standards, the U.S. is throwing away its global advantages. Some scholars even suggest that
educational standards, implemented improperly, can actually hurt U.S. education quality (Zhao,
2009).
Internationally, common educational standards have been a controversial issue. On one
side, scholars like Bill Schmidt have argued strongly for the need of educational standards that
can be shared and applied across states and nations (Schmidt et al., 2009). Plenty of projects
have been implemented to support such a belief. For example, Van Vught and Westerheijden
proposed, as early as 1994, a general model of higher education quality assessment. They
suggested integrating the basic elements of various quality assurance approaches to form
educational standards that can be used cross-nationally (Van Vught & Westerheijden, 1994).
In Europe, the Bologna Declaration of June 1999 put in motion a series of reforms
needed to make European Higher Education more compatible and comparable in the standards
and quality of its higher education institutions. The General Agreement on Trade in Services
(GATS) further promoted cross-border higher education and intensified the argument for crossborder educational standards (Knight, 2003). The Organization for Economic Cooperation and
Development (OECD) is currently carrying out a feasibility study for the Assessment of Higher
Education Learning Outcomes (AHELO), to see whether it is practical and feasible to assess
what students know and can do upon graduation. The AHELO assessment aims directly to
evaluate student performance at the global level and across diverse cultures, languages, and
different types of institutions (Ewell, 2012). UNESCO has also proposed the Guidelines for
Quality Provision in Cross-Border Higher Education (UNESCO/OCED, 2005). These efforts
support the effort to develop quality standards that can be used internationally.

17

On the other hand, there have been strong criticisms against the common standards
concept and framework. One argument is that education quality and quality assurance are
localized matters. Educational quality is shaped and influenced by cultural and social factors.
Educational purposes and practices, including quality standards and quality assurance, are
influenced by contextual factors, such as history, culture, economics, and the political system.
The socio-cultural differences that exist between countries make it challenging to apply quality
standards across countries. Therefore, it appears to be impossible to have a common set of
quality standards (Kogan, 1996). Critics of common standards have also pointed out the
potential dangers, one of which is in exporting quality assurance and accreditation systems from
the industrialized world to developing countries (Lim, 1999). Rogers, Graham, and Mayes (2007)
noted the great amount of content that is being developed by the West and transported via the
internet to the rest of the world. They argued that the trend highlights the need to explore
international quality standards more carefully; more thought needs to be put into cultural and
social differences and their influence on the divergence of quality definitions.
Riyad Shahjahan reviewed the International Assessment of Higher Education Learning
Outcomes (AHELO) texts. He identified two themes that speak against cross-border educational
quality standards: 1) crisis and imperial logic in policy production; and 2) Anglo-Eurocentrism in
global designs and colonial relationships. He argued that through AHELO, the Organization for
Economic Cooperation and Development (OECD) is striving to construct a global space of
equivalence for teaching and learning in higher education, and in so doing, is perpetuating
coloniality in global higher education (Shahjahan, 2013).
In summary, there are two opposing views on whether there should be common crossborder educational standards. Even though there are needs and desires to have common quality

18

standards, the contextual differences and other challenges make it difficult to develop and
implement them. In terms of quality definitions, purpose, functions, methods, and focus
(Middlehurst, 2001), the current international regulations take many forms and are often
fragmented, disorganized, uncoordinated, and ineffective (McBurnie & Ziguras, 2007). It is far
from certain that a model that suits one country is also optimally suited to another country.
As universities become increasingly global actors and extend their influence
internationally (Amey, 2010; Irele, 2012), the lack of a shared quality framework, together with
the diversity and unevenness of quality assurance practices, creates knowledge gaps and
unknowns in cross-border higher education. One of the unknowns is how much these quality
standards, as developed by U.S. organizations, can be applied in educational settings other than
the U.S. As higher education increasingly becomes globalized, there will be more educational
exchange, more cross-border program offerings, and more international trade in the form of
educational services (Cremonini, Epping, Westerheijden, & Vogelsang, 2012). In order to
ensure the consistency of program quality, there is a need to know how U.S. quality standards
can apply to other countries.
2.2 Quality Standards for Online Education in the U.S.
Current U.S. quality assurance practices occur at three levels. First, college faculty are
the primary quality assurance group. A series of activities happen at the department level, led
by faculty: establishing learning objectives, managing the process for students to learn, and
evaluating learning outcomes through exams and other forms of testing. Second, institutions
ensure that departmental processes are in line with university requirements (such as faculty
hiring, resource allocating, and program offerings). Third, external quality assurance
organizations (regional accreditation agencies, state government, and discipline-specific

19

accreditation organizations) oversee these institutional processes by asking whether the other two
levels of quality assurance work in practice. The regional accrediting bodies create standards for
evaluation for education programs (Howell, Baker, Zuehl, & Johansen, 2007).
The academic structure in China is similar to what exists in the U.S. Quality assurance
practice in China also has institutional and faculty components (Pan, 2006). What is missing in
China is the third component of external quality assurance and related standards that are
commonly present in the U.S. Therefore, this study focused on the last level of quality assurance:
quality standards and indicators for evaluation purposes. The researcher’s first task was to
identify publications to represent quality standards in the U.S.
Quality Standards Developed by U.S. Organizations
The Western Cooperative for Educational Telecommunications (WCET) drafted an
online education quality standard, titled Best Practices for Electronically Offered Degree and
Certificate Programs, in 1995. This standard was developed to demonstrate how wellestablished essentials of institutional quality would apply to online learning programs. It was one
of the first attempts to identify and assess the quality issue in online education. Three categories
of quality evaluation were identified: curriculum and instruction, institutional context and
commitment, and evaluation. The guidelines were expanded into five categories by adding
faculty support and student support (WCET, 2001). Consequently, the updated WCET
guidelines have been one of the most cited quality standards in the online education field. The
key elements from WCET have been adopted widely by other organizations since 2001.
For the same purpose of accreditation, the New England Association of Schools and
Colleges Commission on Institutions of Higher Education (CIHE) proposed a set of guidelines to
evaluate online program quality (CIHE, 2009), in which eight categories were presented with

20

guidelines that institutions can use to measure online program quality. Similarly, the Council of
Regional Accrediting Commissions (C-RAC) also proposed guidelines to evaluate the quality of
online learning, in which eight categories were presented with detailed aspects that institutions
can follow (C-RAC, 2009).
Online Education Quality Standards Research
The Sloan-Consortium (Sloan-C) is an organization dedicated to improving the quality of
online education. It published a quality framework for online learning, which describes five
basic elements needed to have a good online learning environment. These five components of
quality are learning effectiveness, student satisfaction, faculty satisfaction, scale, and access
(Moore, 2005).
Researchers have also joined the effort to explore the legitimacy and effectiveness of
quality standards for online education. Chaney and colleagues (Chaney et al., 2009) searched 10
electronic databases with the keywords “quality” and “distance education,” which yielded 165
articles and 12 books. They reviewed this pool of sources and gathered information on the
quality indicators and benchmarks of distance education. The result was a list of quality
indicators published in the final report titled A Primer on Quality Indicators of Distance
Education. They argued that this is a set of online education quality indicators that all parties
deem important in designing, implementing, and evaluating distance education courses and
programs.
Using a similar research method, Lockee and colleagues (2010) examined online
education standards maintained by a variety of organizations, focusing on commonalities and
differences with regard to instructional design specifications and processes. The report,
Organizational Perspectives on Quality in Distance Learning, categorized six quality elements.

21

More recently, using a meta-analysis approach, Boston and colleagues (2011) found, from 13
different published peer-reviewed studies, that scholars’ views of a quality online program have
many commonalities. Institutional commitment, leadership, and support were the most cited
themes as part of quality measurement. Teaching and learning was the second most cited theme
for indicating quality. The third most cited component was faculty support, student support, and
the course development themes.
More recently, Kaye Shelton compared 13 online education standards from the literature
to identify the quality elements of online education programs (Shelton, 2011). In her article, “A
Review of Paradigms for Evaluating the Quality of Online Education Programs,” she concluded
that online education programs have many commonalities. Again, institutional support and
leadership were the most cited elements when determining online education program quality.
The factor of teaching and learning was the second most cited element (Shelton, 2004).
In the area of online education quality study, Quality on the Line is a well-known study
which has gotten a lot of attention and publicity. It was conducted by The Institute for Higher
Education Policy, commissioned by the National Education Association (NEA) and Blackboard
Company. The goal of the study was to validate published online education quality benchmarks.
The study examined the applicability of common quality benchmarks available at the time, and it
confirmed that most of the benchmarks were appropriate and necessary to ensure the quality of
online education programs. The study had three components. First, a comprehensive literature
search identified benchmarks recommended by other organizations, as well as those suggested in
various articles and publications. The study then identified institutions that have had substantial
experiences in online education. The last component was to visit six U.S. institutions to assess
the degree to which the campuses incorporate the benchmarks in their online programs. During

22

the visits, a survey was administered to faculty, administrators, and students in order to
understand how stakeholders perceive the importance of these benchmarks.
Quality on the Line brought research data to the heated debate about online education
quality, and it provided a more tangible meaning of quality assurance. This study revealed that
most of the assembled benchmarks were considered important by stakeholders. The final report,
Quality on the Line, listed 24 benchmarks from seven categories (Phipps & Merisotis, 2000).
These quality benchmarks were subsequently adopted by the National Education Association.
Institutions have striven to incorporate these benchmarks into their policies, practices, and
procedures.
Quality Indicators and Categorization
In order to understand better the aspects of these quality indicators, most U.S. quality
standards use a categorization to represent the factors that contribute to the quality of online
education. A typical categorization has five to eight themes: institutional support, faculty
support, student support, course development, teaching, and evaluation. Chapter Three describes
in detail how this study proposed a categorization and organized quality indicators into themes.
Table 1 lists some of the online education quality indicators identified from literature (Chaney et
al., 2009).
Table 1. Common Quality Indicators of Online Education

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2.3 Online Education in China
The Current State
Chinese online education started by following the model of the British Open University,
an online institution that was founded in 1969 on the belief that technologies can bring learning
to students who would otherwise not have the opportunity for a college education (Katz, 2008).
By adopting open admission and learning practices, the British Open University permits all kinds
of students to learn, without barriers from age, gender, or time constraints (Cooper, 2010).
British Prime Minister Mr. Edward Heath visited China in 1978 and shared the success story of
the British Open University with Mr. Deng XiaoPing (then leader of China). Mr. Deng proposed
the establishment of China Central Radio & TV University (CRTVU), with the purpose to use
this education model as a way to widen higher education access. This was the advent of Chinese
distance education. The purpose of Chinese online and distance education programs is defined
to serve Chinese society by meeting the needs of lifelong education (MoE, 2010). The focus of
Chinese online education is on serving working adults, integrating theory and practice, and
educating students with applicable skills (Hao, Feng, & Chao, 2007). The curriculum is open
admission (no national college entry exam required), and open campus (not limited to a certain
campus or city), and there is a flexible timeline to finish (within eight years to graduate).
Course offerings are flexible, depending on market needs. There is also a major
pedagogical shift. In the traditional Chinese instruction, instructors give lecture style instruction
to deliver content knowledge. Online classes have changed this teaching style of instruction, by
adopting a self-guided learning format. Instructors provide guidance in this process. Classes are
mostly in a self-directed asynchronous format. Most course materials are available on the
internet. The learning is self-paced, and interaction is encouraged among students. Support is

24

available through local learning centers, and through email and online discussion with the
instructors and teaching assistants.
Students enrolled in Chinese online programs are mainly part-time working adults. This
group of students may have failed the national college entry exam, and therefore did not have
opportunities to attend traditional campus-based high education. They are not as academically
strong as their counterparts from the traditional age of college students. Attending school parttime means more challenges to balance study and other life obligations (Fidishun, 2000). To
help adult part-time online students succeed, institutions evaluate these students’ ability and
preparation at the beginning of their online program. Based on their preparedness, there could be
remedial courses added.
The instructors for the online courses are mostly recruited from the national universities.
Most instructors are also teaching the same title course in a face-to-face format. In addition to
teaching, they are actively engaged in research and in other scholarly work. They are teaching
online courses as an additional workload that is not considered for faculty promotion evaluation.
They are being paid with stipends for their work.
Online Education Quality Assurance in China
As China expands its online education to increase access to higher education, quality has
become a significant concern to Chinese online education stakeholders. In response, the
Chinese Ministry of Education (MoE) ceased granting approval for new online programs in 2003.
The MoE introduced the Annual Reporting and Censorship procedure, which involves annual
internal reviews by institutions and external audits by the Distance and Continuing Education
Office of MoE (Jung et al., 2011). A closer look at the procedures, however, reveals that the
data collection focused on the demographics of programs, not on teaching and learning processes.

25

The existing Chinese way of collecting data for evaluating online programs, according to
Chinese scholars, emphasizes infrastructure and program management. There is no indication
that the collected data is used for for quality improvement. Chinese scholars have suggested that
China should develop more comprehensive online education quality standards and procedures, so
that the emphasis can gradually shift from governmental management to the teaching and
learning process, and to the effective use of resources (Guo, Huang, & Chen, 2009).
Because online education is a recent development in China, there are not Chinese quality
standards that are comparable to the standards developed by U.S. organizations. There is no
Chinese regional accreditation agency, nor a Chinese organization like SLOAN-C, to coordinate
and develop comprehensive quality indicators. The existing Chinese way of collecting data for
evaluating online programs, according to Chinese scholars, emphasizes infrastructure and
program management.
Chinese scholars have suggested that China should develop more comprehensive online
education quality standards and procedures, in which the emphasis should gradually shift from
governmental management to the teaching and learning process and to the effective use of
resources (Guo et al., 2009). When Chinese scholars and institutions begin to explore and
develop online education quality standards, an understanding of quality standards and quality
assurance practices are theoretically valuable and empirically necessary. It is beneficial for
China to learn about good practices from the international academic community.
Why Should China Care about the U.S. Quality Standards?
As the Chinese learn about best practices for quality assurance from the international
community, they find many sets of them. There is a variety of best practices that exists
internationally with respect to online learning. The U.S. is one model, but Europe can be another.

26

With so much information available, how do the Chinese decide which one to choose? For the
scope of this study, the researcher first chose the U.S. standards, because U.S. higher education
is highly regarded worldwide, especially in China (ARWU, 2013), with the goal to include
standards from other regions in the future. Before going any further with the exploration of the
applicability of the U.S. quality standards to China, it is important to think about the theoretical
perspectives of educational quality in general.
2.4 Chapter Summary
The quality of online education programs is a complex and difficult concept to define and
to measure. It depends on a range of factors, from the student, the curriculum, the instructional
design, technology, and faculty quality (Meyer, 2002, p. 101). The understanding of online
quality definition and measurement relies on the specifics of the purpose, the perspective, and the
levels (Benson, 2003).
Many U.S. organizations and accreditation agencies have developed quality guidelines
that detail the essentials of what a quality online program should be (Moore, 2011). Many
studies have been conducted to explore the legitimacy of these quality standards. After years of
experiences, these U.S. standards could serve as great resources for other online programs that
are in the process of improving quality. While these quality standards do help online programs,
there is still a lot more to be learned about the practical use of these standards. One of the
unknowns is to what degree these quality standards, as developed by U.S. organizations, can be
applied in educational settings other than the U.S. As the globalization trend in higher education
continues, cross-border higher education has become a key element of internationalization in
higher education (Cremonini et al., 2012). In order to have consistency in our programs and to

27

explore the possibility of a global common online education program standard, it is important to
know whether quality standards developed in the U.S. can be applied in other settings.
At the same time, China is in need of quality standards to help ensure the quality of its
online education. Chinese scholars are learning from the best practices of the international
community to help with their process of standards development. As discussed above, the
concept of quality is complex and is often a local matter. Considering the major differences that
exist between the U.S. and China (socially, politically, and culturally), it is difficult to know for
sure how U.S. quality standards might apply to Chinese educational settings. This study aims to
answer part of this question, by exploring faculty perceptions, so that U.S. quality standards can
be successfully adopted by the Chinese with confidence. Chapter Three discusses the research
methodology.

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CHAPTER 3
Methodology
3.1 Introduction
In order to maximize the potential of online education, Chinese institutions are looking
for ways to improve the quality of online education. One of the tasks is to develop quality
standards that detail the essential criteria for online programs. The Chinese government has also
suggested that Chinese institutions learn about good practices from other countries and connect
to the international academic community. In order to be of any value, though, these international
standards and benchmarks first have to be considered valuable and relevant by Chinese faculty.
The purpose of the study was to investigate to what extent the online quality benchmarks
developed by U.S. organizations are perceived as relevant by Chinese online faculty. The study
set out to replicate the Quality on the Line study (Phipps & Merisotis, 2000), with some
variations. First, the study included the U.S. online education quality standards developed over
the past 13 years, and the assembled benchmarks reflect the quality criteria changes which
happened during this period. Second, the study put the U.S. online education benchmarks to test
in international settings, by surveying Chinese online faculty perceptions of these benchmarks.
The Chinese faculty responses help the U.S. to view our quality standards from a different
perspective. Third, unlike Quality on the Line, this study focused on faculty, because the faculty
group is the most relevant group for online program quality. By studying faculty members’
perceptions, the study has a more focused target group. That is not to say that other groups’
opinions are not important; those opinions could be significant, but they necessitate separate
studies. This study aimed to answer the following research questions:

29

Research Questions
Do Chinese faculty perceive U.S. quality standards for online education as relevant?
Sub-questions:
1. To what extent do Chinese faculty perceive the importance of U.S. online program
quality indicators?
2. To what extent do Chinese faculty perceive the presence of U.S. online program
quality indicators?
3. Is there any association between participants’ satisfaction level for online teaching
experience and the perceived presence of the quality indicators?
4. Is there any association between participants’ level of concern for quality and the
perceived presence of the quality indicators?
5. Do the U.S. quality indicators included in this study sufficiently capture the essence
of quality for online education?
This study used exploratory approach to investigate how quality standards from the U.S.
are perceived by faculty from China. In this study, the degree of relevance was explored through
faculty members’ perceptions, at two levels. First, in order for these benchmarks to be of any
value to online education in China, they have to be perceived by Chinese faculty as important.
Second, if these quality indicators are relevant to Chinese online education, they need to be
present in Chinese online programs’ quality assurance practices. Therefore, the study explored
the perceived importance and the actual existence of these quality indicators in China, through
the ratings and the answers to open-ended questions. In the study, the term “benchmark” and
“indicator” are used interchangeably, referring to the U.S. quality indicators selected for the
study.

30

The study used a survey to collect both quantitative data (ratings) and qualitative data
(open-ended question responses). The survey was administered at a Chinese national university
to a group of selected Chinese faculty who were involved in online teaching. Even though the
study was based on individual faculty members’ perspectives, the unit of analysis was the online
program at this Chinese institution. This chapter discusses the context of the study, the processes
to develop the survey, and the strategy to collect and analyze data.
3.2 Context
ABC University was selected as the research site because of its success in online
education by providing access and its desire to continue improving quality. Various procedures
have been designed and implemented by this online program to ensure quality, and several
procedures have been adopted by other online programs in the country (ABC University, 2013b).
ABC University is one of 38 Chinese universities that started offering online programs in
the year 2000. After 13 years of development, the ABC University online program has
demonstrated its effectiveness in providing access and in lowering costs, with its program
officially approved by the Chinese Ministry of Education. In 2012, ABC University’s online
program enrolled 75,000 students in 37 majors (ABC University, 2013a).
The online education curriculum at ABC University is characterized as “teacher-guided
and learner-centered,” which encourage autonomous learning. A typical online course has two
to three credits, and it lasts 15 weeks. There are two semesters, fall and spring, in one academic
year. The spring semester lasts four months, from February to June. The fall semester lasts four
months, from September to January. Students can take classes online, via TV broadcasting
programs, or they can go to one of the 240 study centers to take face-to-face classes. Students
have the choice to study on their own or to collaborate with other students. Its curriculum has a

31

credit system that is similar to that of U.S. higher education institutions. A supervisory
committee is responsible for monitoring the quality of the online program. Committee members
assess teaching quality as well as courseware quality. Exams are used as the main method to
measure student learning. Individually, students also use an Assessment Information System to
self-identify their learning problems and to improve their learning. One such tool in the
Assessment System is the electronic portfolio that students build over the length of their student
career in the program.
Administratively, a two-tier model is utilized. The Online College of ABC University is
responsible for marketing, operation, management, and coordination, while other colleges within
the university are responsible for course planning and instruction delivery, including recruiting
instructors. Externally, institutional partnerships are established to create study centers where
various administrative and academic services are provided. Two hundred forty study centers
have been created in 24 provinces, to provide satellite TV, computer labs, Internet connectivity,
and tutoring services.
3.3 Participants
The participants were recruited by a brochure that was distributed with the final exam
grading materials. The sample population included faculty members and teaching staff who
were involved in undergraduate online programs at ABC University. The sample population in
the study had 400 faculty members and the same number of teaching assistants. Many faculty
members have developed online course materials and are responsible for course content updates.
Some of them are teaching the same course in a face-to-face format. The faculty profile at ABC
University is similar to other Chinese national universities, such as Jilin University in the north,
Yunnan University in the south, and Xiamen University in the east. This study provides a

32

rationale for future studies in which more Chinese institutions, of different types and from
different regions, can be selected to represent Chinese online faculty.
3.4 Survey Development
There were nine processes involved in the survey development process:
1. Prior studies and their findings.
2. Identifying U.S. quality indicators for online education.
3. Expert review of the selection of quality indicators.
4. Survey rating questions.
5. Open-ended questions.
6. Demographic information.
7. Translation and reverse translation.
8. Pilot test.
9. IRB approval.
Prior Study
A preliminary study was conducted to identify areas within online education that had the
potential to be research topics of this study (Dai, 2013). The researcher visited ABC University
in the year 2012 and interviewed faculty, staff, and administrators. The findings from the prior
study helped the researcher understand the Chinese online educational environment, the ABC
University Online College as an organization, and online faculty practices in developing and
teaching online courses. These findings led to the selection of the current study topic and the
inclusion of open-ended questions to complement the fixed answer questions in the questionnaire.

33

U.S. Quality Standards Selection
Nine sources of U.S. literature were identified. The references that led to the
identification of the sources came from the Handbook of Distance Education (Moore, 2013), the
Sloan-C web site and documentation, and frequently cited online quality research articles.
Standards of online education quality published by major online quality assurance organizations
(Sloan-C, CIHE) were the first ones included. Other standards were selected, based on their
influence in online education, their publication date, and their relevance to quality assurance. In
addition, three recent research articles on online education quality were also included. These
nine sources are as follows:
1. Quality on the Line, by the Institute for Higher Education Policy (IHEP, 2000).
2. Guidelines for the Evaluation of Distance Education (CIHE, CRAC, 2009).
3. Quality Guidelines, by Southern Regional Education Board (SREB, 2006).
4. Distance Education and Training Council, by Accrediting Commission (DETC, 2013).
5. SLOAN-C Quality Score Card Tool (SLOAN-C, 2013).
6. Best Practices For Electronically Offered Degree and Certificate Programs (WCET,
2002).
7. Examining Standards For Distance Education Systems (Lockee, 2010).
8. A Primer on Quality Indicators of Distance Education (Chaney, 2009).
9. Benchmarking Quality in Online Degree Programs (Mariasingam & Hanna, 2006).
Analyzing these standards revealed that all nine sources had some kind of categorization
for their included quality indicators. The presence of such categorization was aggregated into a
spreadsheet, as shown in Table 2. Each category was marked with the letter “Y” when it
appeared in the source. The list of categories was filtered by appeared frequency.

34

Table 2. Quality Indicators Category Selection
Sources of quality standards
Category

1

2

3

4

5

6

7

8

# of appearance
9

Y Y Y

Y Y

across 9 sources
7

Y

Y Y

4

Course design and development

Y Y Y Y Y Y Y Y Y

9

Teaching and learning

Y

Y Y Y Y Y

7

Faculty support

Y Y

Y Y Y Y Y Y

8

Student support

Y Y

Y Y Y Y Y

7

Evaluation and assessment

Y Y Y Y Y Y

Institutional support

Y Y

Technology and support

Y

Y

Y Y

8

The categories with the most appearances across the nine sources were included as the
categories in the current study. The cut off frequency of appearance was four. When a category
appeared four or more times across these nine sources, the category was included; otherwise it
was excluded. For example, institutional commitment and support as a category was included
because it appeared in seven out of the nine sources. Course development was also included
because it appeared in nine out of the nine sources. Faculty support was included because it
appeared in eight out of the nine sources. On the other hand, while the cost and scale theme is an
important concept for online program quality, only SLOAN-C listed it as a category, and it was
not mentioned in other sources. Therefore, it was not included in the final list of categories.
Two hundred sixty-five quality indicators were extracted to reflect the current U.S. online
education quality standards and quality assurance practices. These quality indicators were
organized into seven categories. These indicators were further edited by removing redundant
statements, splitting statements with multiple meanings, and merging items with similar
meanings. The result was a set of indicators with 48 statements (Appendix 1). Items 1 – 6

35

represent the institutional support categories; items 7-12 represent the technology category; items
13-20 represent the course design and development category; items 21-31 represent the teaching
and learning category; items 32-37 represent the faculty support category; items 37-42 represent
the student support category; and items 43-48 represent the evaluation and assessment category.
Expert Review
In the current study, content face validity was assured in the process of selecting the U.S.
online quality standards and including the final 48 quality statements. Part of the U.S. online
education standards were developed by professional organizations, such as Sloan-C, which
themselves have a certain credibility and their own sense of validity. The other part of the
benchmarks came from the body of literature that represents a group of scholars’ opinions.
Together, this set of benchmarks has been reviewed and agreed upon by many scholars. They
have also been practiced by many U.S. institutions. The origin and the influence of these
benchmarks ensure that the selected benchmarks represent the quality benchmarks in U.S. online
education.
At the dissertation proposal meeting, the feedback from the dissertation committee was to
“look hard and know why each statement is included.” Based on this suggestion, and to improve
the face validity of the survey, the 48 statements were sent to five scholars in the field of online
education, to see how these indicators appear to experts. The experts received a questionnaire
(Appendix 1), in which they were asked to review each item’s legitimacy in relation to online
program quality, and to comment on whether the benchmark should be included.
Forty-eight quality indicators were included in this questionnaire. An example of a
question is shown in Figure 1. The 48 indicators provided to experts are in Appendix 1. The
experts were asked to provide their opinions on whether each item should be included in the

36

study, and to comment on the choices they make. One important piece of feedback from the
experts was that the categorization was difficult to conduct because some items can belong to
several categories.
Figure 1. The Question Example to Ask Online Education Experts

Based on the feedback and suggestions from these five experts, the quality indicators in
the pool were edited, and the number of statements was reduced from 48 to 31. Table 3 shows
the indicator reduction process and its results. Because the categorization was questioned by the
dissertation committee and the experts, this led to the need to use Confirmatory Factor Analysis
(CFA) to verify the legitimacy of the categorization.
Table 3. U.S. Quality Indicators and Their Categorizations
Benchmark statements category

Initial #

# Indicators

# of Indicators

Institution support

26
indicators.
16

6
for experts.
6

used in4the
3
study.
6

Technology
Course design and development

65

8

Teaching and learning strategy

37

11

5

Faculty support

21

6

5

Student support

53

6

4

Evaluation and assessment

47

6

4

Total

265

48

31

37

Institutional policy and support category includes benchmarks of institutional mission
statement, infrastructure development, resource allocation, and incentives for faculty to get
involved in online education.
1. Online learning is incorporated into the institution’s governance and academic
oversight.
2. Faculty clearly understand the value of online education.
3. The institution provides sufficient resources to support online course offerings.
4. The institution has clear, specific, published policies related to academic integrity.
Technology and technology support category includes benchmarks of policies and
processes regarding technology and technical support, technical standards, technical skills for
teaching and learning, and training.
5. There is a documented technology plan that guides the technology investment choice
and implementation.
6. The institution has clear, specific, and published policies related to the use and
safeguarding of student information.
7. Prerequisite technical skills are identified and clearly stated before faculty teach
online courses.
Faculty support category includes benchmarks of providing support to faculty members
when they develop and teach online courses, both technically and pedagogically.
8. Faculty are assisted in the transition from classroom (face-to-face) teaching to online
teaching.
9. Faculty receive training and materials related to fair use, plagiarism, and other
relevant legal and ethical concepts.
10. Faculty are trained with respect to the best practices of online teaching and learning.
11. There is an active peer-mentoring program for online faculty.
12. Faculty are trained with respect to learner needs.
Student support category includes benchmarks of helping online students to prepare
themselves for online learning, engage faculty, interact with other students, and use resources
such as the library.
13. There is an institutional structured system to address student complaints.
14. The institution provides orientation when students start the online program.
15. Students have access to effective academic, personal, and career counseling services.
16. The institution regularly evaluates the effectiveness of the student support services for
improvement purposes.
Assessment category includes benchmarks related to policies and procedures that address
how the institution evaluates online learning, including assessment of courses, student learning,
and faculty teaching online courses.
17. All students are encouraged to evaluate the course at the end of the class.
18. The results of student assessment are available to the instructor of the course for
improvement purposes.
19. Ongoing assessments are conducted to verify each student's readiness for the next
segment.

38

20. Student opinions are systematically sought as one basis for evaluating and improving
teaching purposes.
Course design and development category includes benchmarks of policies and procedures
that are related to course structure, design, and content development processes.
21. Guidelines are used to design and develop online courses.
22. Instructional materials, course syllabi, and learning outcomes are reviewed
periodically.
23. Online courses are designed to require students to engage themselves in analysis,
synthesis, and assessment as part of their courses and program requirements.
24. Each learning segment has an overview that describes objectives and activities.
25. Expectations for student assignments, grade policy, and faculty responses are clearly
provided in the online course syllabi.
26. Online course design provides opportunities for appropriate instructor-student,
student-student, and student-content interaction.
Teaching and learning category includes benchmarks of online student preparedness,
course learning objectives, faculty teaching practice, and teacher-student interactivity.
27. Netiquette expectations regarding lesson activities and email communications are
clearly stated.
28. Information literacy and communication skills are incorporated and taught as an
integral part of the curriculum.
29. Students are instructed in the proper methods of effective research, including
assessment of the validity of resources.
30. Feedback on student assignments and answers to student questions are constructive
and are provided in a timely manner.
31. Tutoring is available as a learning resource for students.
Survey Rating Questions
The survey included two types of questions. One was to ask the respondent to rate the
importance and the presence of these indicators. The other type was the open-ended questions.
The survey instrument started with a question that asked faculty to rank the perceived importance
of the seven themes that were identified. The order of the survey question (Appendix 4) took the
sensitivity of the problems into consideration (Babbie, 2013). The participants were first asked
to rank the importance of the seven factors. This gave participants an opportunity to provide
their opinion of what matters the most, in terms of online education quality, without the
influence of the other survey questions. Then the participants were asked about the institutional

39

practices related to online education quality. Subsequently the participants were asked about
more sensitive questions related to faculty members’ teaching practices, and the reasons that
explain such behaviors. The rating questions included 31 quality indicators, divided by the seven
categories.
Figure 2 shows one example of the questions that were sent to the experts. In the left
column, the participants were asked to rate the importance of the U.S. quality indicators. In the
right column, the participants were asked to rate the presence of the indicators in their practices.
Figure 2. The Question Example for the Rating of Indicators

Open-Ended Questions
The ratings of the indicators provided the measurement of the Chinese faculty's
perceptions on the importance and the presence of these quality indicators. Because of the
differences between these two countries, there may be nuances of meaning that are not
necessarily illuminated by the ratings. The ratings, as quantitative measures, may not unveil the
details of how Chinese faculty think of these U.S. quality indicators; may not provide the
detailed information about the quality problems as perceived by faculty; and may not show the
suggestions Chinese faculty might have to improve quality. In order to get more detailed
perspectives, qualitative data and analysis were necessary.

40

The researcher designed eight open-ended questions in order to ask the respondents to
explain more fully why they responded the way they did, whether they had any reservations
about their responses, and so on. The open-ended questions provided the respondents with the
opportunities to express their views on issues of quality and quality assurance practices in China
with more flexibility and less restrictions.
The open-ended questions were designed with two focuses: one was on participants’
perceptions of the relevance of these U.S. quality indicators to China; the other was on faculty
satisfaction with their online teaching experiences. Questions 1-6 were located at the beginning
of the survey. These questions asked for the unfiltered opinions on what matters to Chinese
faculty, such as the objectives of online programs (Q1); the perceived characteristics of high
quality online education programs (Q2); the satisfaction faculty may have (Q3); the concerns
faculty may have (Q4); and the suggestions they have to improve quality (Q6). Questions 7-8
were located at the end of the survey. These two questions asked faculty to reflect on the
relevance of these benchmarks to Chinese faculty practice (Q7) and to Chinese online programs
(Q8).
Q1. What are your beliefs about the objectives of online education?
Q2. What are the characteristics of online education programs with quality?
Q3. Are you satisfied with your online teaching experience? Why?
Q4. Is there any concern for the quality of current online education? Why?
Q5. If you have a choice, will you teach an online course in the future? Why?
Q6. What do you suggest to improve the quality of online education in China?
Q7. Do these benchmarks reflect your criteria of quality for online education? Why?
Q8. Should China adopt these selected U.S. quality benchmarks? Why?

41

The format of the open-ended questions took two concerns into consideration. One
concern was that there might be more than one response to the question. To address this concern,
each open-ended question asked the respondents to provide three possible answers. The other
concern was that the respondents might give too little information to form a coherent response
(Schuman, 1966). In this study, each open-ended question balanced these two concerns by
providing respondents with three possible responses; each had space for 20 words (Figure 3).
Figure 3. The Example of Open-ended Questions

There were three open-ended questions designed to explore faculty’s satisfaction about
online teaching and their concern for quality. Faculty satisfaction is considered an important
factor in the quality of online learning (Bolliger & Wasilik, 2009; ChanLin, Hong, Horng, Chang,
& Chu, 2006; Moore, 2011). Faculty satisfaction can contribute to many factors, including the
factors listed in this study (institutional policy and support, faculty support, student support,
teaching and learning, and assessment). Therefore, the researcher wanted to explore the
relationship between the level of faculty satisfaction for online teaching and the presence of the
quality indicators, to see whether there was any linkage between the quality indicators and
faculty satisfaction.
The result of having or not having association partly reflected the relevance of these
quality indicators. If there is an association between the practice of these indicators and the
faculty satisfaction level, it could be argue that this implies that there is a need to practice more
of these indicators; if the practice of indicators is found to have no relationship with faculty’s

42

satisfaction, the finding could cause reasonable doubt about the relevance of these quality
indicators.
With a similar goal, the researcher explored the association between the level of concern
for quality and the practice of the quality indicators. The idea was that if these quality indicators
are indeed relevant to Chinese online education, one would expect to see the result of having an
association between the practice of these indicators and the concern for quality.
Demographics
In addition to these 31 quality indicators and the open-ended questions, demographic
information was collected, including the faculty members’ gender, discipline, years of teaching
in higher education, and years of teaching online courses. In this study, demographic
information was collected to provide a feel for the nature of the population. It was not intended
to look at the individual differences among the respondents in terms of their perceptions of
quality.
Translation and Reverse Translation
Because the survey took place in a foreign country and the data was collected in Chinese,
translation work was necessary. To ensure translation accuracy, the survey instrument was
independently translated by two scholars. The translation differences were discussed, and the
revised Chinese version was sent to another translator for reverse translation. The reverse
translation results were compared with the original 31 English statements, to ensure that the
meaning was the same (Appendix 2).
Pilot Survey
A pilot was conducted with three Chinese visiting scholars at Michigan State University.
The three scholars were asked to take the survey and to verbalize their thoughts while they

43

answered the questions. The cognitive interview process was to ensure that the questions were
easy to understand and could be interpreted without ambiguity. This process enabled the
researcher to understand how individuals interpreted the questions (Willis, 2005). Based on the
feedback, the instrument was revised and made ready for field testing. The cognitive interview
protocol is in Appendix 3.
The times that the three visiting scholars took to finish the survey were 20 minutes, 23
minutes, and 28 minutes. This sense of time provided information about the properness of the
length of the survey and the expected time to finish. Based on the piloting test, the expected time
to finish the survey was 25 minutes. The final Chinese version of the survey is in Appendix 3.
A matching English version of the survey is in Appendix 4.
IRB Approval
The current project used an anonymous survey to gauge online instructors' perceptions of
quality indicators. The subjects were not identifiable during the data collection or analysis
processes. The questions had no sensitive information as defined by Michigan State University’s
sensitive data management policy. In addition, each participant was informed of the purpose of
the study and the likely impact on participants (Creswell, p. 64). At the beginning of the survey,
participants were also informed of their right to participate voluntarily, and their right to
withdraw at any time. The participants were also informed that the data would only be viewed
by the researcher and would not be shared with other individuals or other institutions. In order to
ensure confidentiality, the survey excluded any sensitive information. The collected data was
stored in a secure server at Michigan State University, and it will be kept for three years after the
project is finished. The study plan was approved by Michigan State University’s Institutional
Research Board.

44

3.5 Data Collection
The participants were recruited by ABC University. A request was sent to the ABC
University’s Online College to gain access to the sample. After the approval, the survey was
administered using a secured online survey application called Qualtrics. The survey was first
sent to 20 ABC University faculty members who were teaching online courses in the fall of 2013,
with the hope to uncover some survey implementation issues. There was no response after a
week. That prompted a strategy change, in which a decision was made to offer a small incentive
to the participants. Each faculty participant was provided with a $10 value gift card after the
survey was completed. The survey advertising brochure was delivered with the semester final
exam grading material to the faculty who were teaching online courses. At the same time, the
College of Online Education posted an announcement in the Course Management System (CMS),
which contained the web link to the survey. An email reminder was sent out one week later and
the second reminder was sent out one week after that, as the last reminder.
3.6 Data Analysis
Data Cleaning and Preparation
After the survey was closed, the data was exported from Qualtrics into an Excel file. The
survey received a total of 338 returns. The first task was to decide which returns to include in
the analysis. During this process, Excel was used to help understand the data distribution, and
descriptive statistics were calculated. The results showed some respondents did not finish the
survey, and other respondents finished it in a very short time (less than 5 minutes). A data
scrubbing process was used to decide what cases to include in the analysis.
One of the criteria for data cleaning was that if the respondents repeated the same rating
for more than four indicators, then the case was excluded. This was based on the assumption

45

that if a respondent repeatedly provided the same rating for different indicators, then the answers
may not accurately reflect his/her opinion. Therefore the responses should not be included.
Another criterion was about the time it took to finish the survey. If the respondents took less
than six minutes to complete the survey, then the case was not included. The assumption was
that when the time spent to finish the survey was far less than the expected 25 minutes, the
respondents did not spend enough time reading and reflecting on the questions, and paying
attention to the answers. Therefore the responses may not reflect his/her real opinions. After
filtering out the unwanted data, 307 responses were retained for statistical analysis.
Statistical Analysis
Based on U.S. documentation, seven factors/categories were proposed by scholars.
These seven factors are considered by U.S. scholars as important to the quality of online
education programs. But this categorization had not been verified with empirical data. The lack
of verification prompted wondering about whether such categorization was logical. During the
proposal meeting for this study, committee members also wondered about the rationale of the
categorization, because some indicators can belong to multiple categories. Later the experts also
questioned whether the categorization of benchmarks was logical. Are these seven categories
also perceived by Chinese faculty?
Confirmatory Factor Analysis (CFA) was used to verify whether the seven categories fit
the data. The result was that the seven factors/categories did not fit well with the data. The
categorization was modified; the number of factors was reduced from seven to five; and the
indicators were reduced from 31 to 19. The results show that the modified five factors
categorization reasonably well fits with the data and can be used to study the quality of online

46

education in China. In the new categorization, each factor includes three to four quality
indicators. The mean of these indicators was used for further statistical analysis.
To gauge the perceived importance and the presence of these quality indicators, the mean
of the indicators within each factor was used. The association between the perceived importance
of these indicators and the demographics was also explored. Lastly, an analysis was done to
explore the association between the presence of these quality indicators and faculty satisfaction
and concern for quality.
Open-ended Question Analysis
The eight open-ended questions were analyzed using a coding process (Lombard et al.,
2006; Artstein and Poesio, 2008). First, all of the responses were carefully read. Each response
was given a code for possible themes. All responses for the same theme were grouped together,
and a descriptive statement was composed to represent the meaning. After the coding, 10% of
the coding results were randomly selected to be verified by an independent scholar. The results
from the independent coder were compared with the results coded by the researcher, and a few
modifications were made. Detailed results for the open-ended questions are in Appendix 8.
Post Hoc Analysis
After the quantitative and open-ended question analysis, there were two unexpected
findings that emerged from the analysis: the first was the “management factor” in online
education. Management has not appeared often in the U.S. literature as a factor for the quality of
online education. The Chinese faculty from the study, however, mentioned it as very important
in their program. Why did the Chinese emphasize so much the function and the importance of
the management factor? A post hoc analysis was conducted to look for possible reasons.

47

The second unexpected finding was that the data did not fit with the original seven
factors model. After the factors were reduced from seven to five, the model had a better fit. If
the number of quality factors was reduced to get a better fit in the previous analysis, could the
five factor model be reduced even more? Will fewer factors provide more informed findings?
The researcher conducted a post hoc analysis for the five factors to see whether the five factors
could be grouped in anyway.
Bias
The researcher’s bias and subjectivity was born in mind during the study design, data
collection, and data analysis. Because of the researcher’s working experience of supporting
educational technology and online education, the researcher tried to put his own opinions aside;
instead, the researcher focused on using the data and evidence to make decisions. During the
research design process, efforts were made to ensure the selection of unbiased quality indicators.
Five experts in the field of online education were invited to provide scrutiny on each indicator.
The feedback and comments from the experts were used to reduce the indicators from 48 to 31.
The comments from the experts about the quality factors were used later in the data analysis to
confirm the model. After the development of the survey, the survey was piloted by three visiting
Chinese scholars, who provided their opinions on the design and the content of the questions.
The most likely place where bias could have happened in this study was the process of
data analysis. The researcher made an effort to ensure that the findings and the conclusions were
not based on pre-existing knowledge or opinions, but on the data itself. In the discussion section,
interpretations were made to connect this study with other studies in the field. The researcher
also discussed the findings with other scholars, including a trip to the ABC University in 2014.
The feedback from other scholars challenged the researcher’s thinking and provided alternative

48

points of views. This process improved the reasoning of the conclusions and reduced the
likelihood of bias.
3.7 Chapter Summary
This chapter presented the methodology employed in the study, including the survey’s
development and the techniques for analyzing the data. Chapter Four presents the findings from
data analysis.

49

CHAPTER 4
Presentation of Findings
The purpose of the study was to understand whether the U.S. quality standards for online
education are perceived as relevant by Chinese online faculty. The specific research questions
were: 1) To what extent do respondents perceive U.S. online education quality benchmarks as
important? 2) To what extent do respondents perceive the presence of these quality benchmarks?
3) Is there any association between participants’ satisfaction level for online teaching experience
and their perceived presence of the quality indicators? 4) Is there any association between
participants’ level of concern for quality and their perceived presence of the quality indicators?
5) Do the U.S. quality indicators included in this study sufficiently capture the essence of quality
for online education?
Part one (4.1) presents the results from statistical analysis, including model fitting results
and a five factor model for the quality of online education. Part two (4.2) presents the findings
from the open-ended question analysis, to explore further the relevance of the U.S. benchmarks
to Chinese online education. Part three (4.3) presents the findings from post hoc analysis,
including the management factor and further reduction of factors.
4.1 Statistical Analysis
Respondents’ Characteristics
A survey was administered at a Chinese institution. As shown in Table 4, the survey
participants were evenly divided by gender; 48% of the respondents were female and 52% were
male. Forty-six percent of the respondents were between 35 and 50 years old, and 6% were over
50 years old. In terms of academic ranking, 47% were faculty (professor and associate
professor), and 53% were teaching assistants. The respondents came from all academic

50

disciplines, with the exception of military study. Thirty percent of the respondents were from the
education discipline, 14% from engineering, 10% from literature, and 10% from science. The
disciplines with the lowest percentage were history (3.8%), philosophy (3%), and law (2.4%).
In terms of teaching experience, 43% of the respondents had worked less than five years
in higher education institution(s), 32% had worked six to ten years, and 25% had worked over
ten years. Fifty percent of the respondents had less than two years of online teaching experience,
30% had less than five years, and 20% had more than five years. Most of the respondents (87%)
were also teaching the same course in a face-to-face format. Sixty-three percent of the
respondents’ online courses had fewer than 100 students, 24% had 100 to 200 students, and 13%
had more than 200 students.
Table 4. Summary of the Respondent Demographics
Gender

51% Male

49% Female

Age

47% < 35 years

46% 35-50 years

7% > 50 years

Teaching at college level

43% 1-5 years

32% 6-10 years

25% > 10 years

Academic ranking

13% professor

34% associate Prof

53% instructor

Years of teaching online

50% 1-2 years

30% 3-5 years

20% > 5 years

Do you teach face-to-face? 87% yes

13% no

Course type

25% introductory

44% major required

31% both

# online course (s) taught

66% 1-2 courses

30% 3-4 courses

4% > 4 courses

Average # of students

63% 1-100

24% 100-200

13% > 200

Times repeated the course

33% 1-2 times

28% 3-4 times

39% > 4 times

Categorization for the Quality Indicators of Online Education
Chapter Three proposed a seven factor categorization that has 31 quality indicators which
underlie the quality of online education in the U.S. This section describes the model fitting
process which was used to confirm the proposed model for the quality of online education in

51

China. This process empirically tested the proposed quality model against the data collected
from the survey.
In the survey, the participants were asked to rate their perceived importance of each
quality indicator. Each rating was between 1 and 4, where 1 represented the perception of “not
important” and 4 represented the perception of “important.” When the answers were left blank
or selected as “not sure,” the responses were marked as missing data. Of the perceived
importance ratings, the missing data ranged from 1% to 4%. The missing data was excluded
from the analysis. Confirmatory Factor Analysis (CFA) was used to verify whether the
preliminary model fit with the collected data. Pearson's chi-square (χ2), the Comparative Fit
Index (CFI), and the Root Mean Square Error of Approximation (RMSEA) were used to
determine the model’s fit with the data.
Pearson's chi-squared test (χ2) is a classic goodness-of-fit tool. The null hypothesis was
that the implied or predicted covariance matrix Σ is equivalent to the observed sample covariance
matrix S, which means Σ=S (Albright & Park, 2009). When χ2 is <.05, the null hypothesis can
be accepted with confidence. However, the χ2 tool is sensitive to sample size (Albright & Park,
2009). This study additionally used the Root Mean Square Error of Approximation (RMSEA) to
measure the fitness, because RMSEA is not sensitive to sample size. The Comparative Fit Index
(CFI) was another tool used to evaluate the fitness of the model. The possible value of CFI
ranges from 0 to 1, where 0 indicates a poor fit and 1 indicates a perfect fit.
In order to claim the model has a reasonably good fit, the ideal results are: χ2 < .05,
RMSEA <= .06, and CFI >= .95 (Albright & Park, 2009; O’Rourke & Hatcher, 2013). The
results from the initial CFA showed that the RMSEA = .060 and CFI = .88 (shown in Appendix
6). The initial categorization thus did not fit the data very well. In order to get a better fit

52

between the model and the data, the model was modified, in order to get the CFI larger than .95
and the REMSA less than .060.
Category Reduction
During this modification process, there were four things considered. The first was how
many factors were needed to represent the quality of online education. The initial CFA results
showed that the “Institutional support” factor and the “Technology” factor were highly correlated
(the correlation was .94); these two factors were combine into one, labeled as the “Institutional
support” factor. Similarly, the “Course design” and the “Teaching and learning” factor were
highly correlated (the correlation was .94). These two factors were combined into one, labeled
as the “Teaching and learning” factor. After this process, there were five factors left in the
model: institutional support, faculty support, student support, teaching and learning, and
assessment.
Quality Indicator Reduction
To achieve a better fit between the model and the data, the quality indicators with lower
standardized factor loading (< .60) were removed one by one. After each removal, CFA was
conducted to check the key parameters of χ2, CFI, and REMSE. This process was repeated until
the key parameters met the requirement. During this indicator reduction process, each factor was
to have at least three indicators, and the total benchmarks in the model should be below 20
(O’Rourke & Hatcher, p. 192). At the same time, attention was paid to ensure that the items
within each factor were content-wise consistent.
The CFA results (Table 5) showed that the Comparative Fit Index (CFI) = .95 (>=.95
preferred). The Root Mean Square Error of Approximation (RMSEA) = .050 (=<. 055 preferred),
and the upper bound 90% confident limit for RMSEA was .06 (=< .09 preferred), and P=.000).

53

Comparing the results against the suggested characteristics of an ideal fit (Hatcher 2013, p. 250),
the collected data had a good fit with the five factor model.
Table 5. The CFA Results

Internal Consistency Test
Before accepting this modified model, however, Cronbach’s Alpha tests were conducted
to assure the internal consistency of the items within each factor. For the items within each
factor to be consistent, Cronbach’s Alpha, as an index of internal consistency, needs to be
greater than .70, with the ideal range being .80 to .90 (O’Rourke & Hatcher, 2013).
Table 6. The Reliability Test Results for Five Factors
Factor

# of indicators

Cronbach's Alpha

Institutional support

4

0.73

Faculty support

4

0.72

Student support

3

0.76

Teaching and learning

4

0.80

Assessment

4

0.79

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In Table 6, the reliability tests show that all five factors’ estimated internal consistency
were within the acceptable limits (i.e. alpha >= .70). There were two factors (teaching and
learning, assessment) for which the Cronbach’s Alpha values were close to .80, which indicated
the items within these two factors were highly consistent.
After the CFA analysis, the content consistency checking, and the Cronbach’s alpha
internal consistence test, the modified five-factor categorization (shown in Figure 4) had a
reasonably good fit with the data. This categorization originated from the U.S. literature and was
confirmed as a fit with the data collected. For the rest of the analysis, this categorization was
used to measure the quality of online education in China.
Figure 4. Five Factor Categorization for Quality Indicators

Description of the Categorization
The following section describes the content of this model and its quality indicators. This
model has five factors or components: institutional support, faculty support, student support,
teaching and learning, and assessment. Each factor includes three to four quality indicators. The

55

institutional support factor has four benchmarks that reflect institutional support. The first
indicator is about faculty’s understanding of the value and purpose of online education in China.
The underlying statement is that in order for an online program to have quality, each faculty
member needs to understand the purpose and objective. The level of understanding reflects the
institutional support and practice of promoting and supporting online programs.
In addition, institutions need to ensure sufficient resources for online course offerings.
As resources become scarce in higher education, online education is competing with other
programs for resources. The third benchmark is to have clear, specific, and published policies to
safeguard student information. When student information becomes available online, it is easy to
access anywhere and anytime. But the convenience also makes it easier to lose student
information if a security breach happens.
The faculty support factor includes four indicators, which require that assistance be
provided to faculty in the transition from face-to-face teaching to online teaching. One format of
the assistance is through faculty professional development, through which faculty are trained
with respect to the best practices of online teaching and learning.
The student factor includes three indicators, which ensure that the institution provides
orientation when students start the program; that students have access to effective academic
service and career counseling services; and that there is an institutional structure to address
student complaints.
The teaching and learning factor includes four indicators that cover course design, course
content development, and the teaching and learning process. For example, each learning
segment needs to have an overview that describes the objectives and activities. The courses
should be designed to require students to engage themselves in analysis, synthesis, and

56

evaluation as part of their learning requirements. The evaluation and assessment factor includes
four indicators. For example, there should be ongoing assessments to verify each student's
readiness for the next segment. All students are encouraged to evaluate the course at the end of
the class. The results of course evaluations are available to the instructor for improvement
purposes.
Ratings for the Importance of the Quality Indicators
The respondents were asked to rate the importance of each indicator statement on a scale
of 1to 4, where 1 is “Not important,” 2 is “Slightly important,” 3 is “Relatively important,” and 4
is “Important.” The average of the ratings within each factor was used to represent the rating for
the factor. The missing data was excluded from calculation. a
Table 7. The Distribution of the Perceived Importance
Factor

<2.5

2.5 - 3

3 - 3.5

3.5 - 4

3.6%
Less
6.3%
Important
4.9%

13.4%
Slightly
18.4%
Important
23.0%

27.0%
Moderately
29.0%
Important
13.4%

56.0%
Very
46.3%
Important
58.7%

Teaching and learning

4.9%

16.6%

24.8%

53.7%

Assessment

4.9%

16.7%

26.6%

52.3%

Institutional support
Faculty support
Student support

From the summary of the statistics in Table 7, the ratings for the factors ranged from 1.5
to 4.0. The distribution was positively skewed, which means more respondents rated indicators
with high ratings. Using “Institution support” as an example, the ratings indicated that more than
half (56%) of respondents gave the institutional support factor a rating of 3.5 to 4.0 (Table 7).

a

Institutional support had one missing response; faculty support had two missing responses; and
the assessment factor had nine missing responses. Every factor’s missing data percentage was
below 5%.

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When the factor had four items, a score of 3.5 and above means a respondent needed to
rate two indicators as 4.0, and two as 3.0, which is a strong indication that faculty agreed with
the importance of the factor and the indicators included. Another 27% of respondents rated 3.0
to 3.5. Combining these two groups, 83% of the respondents rated the institutional support
factor a 3.0 and above, indicating a strong perception of the importance of the factor. Only 3.6%
rated this factor as below 2.5. The other four factors’ ratings presented a similar distribution
pattern. The results from Table 7 show that the majority of respondents perceived all five factors
as important.
Ratings for the Presence of Quality Indicators
The survey also asked the respondents to rate the presence of these U.S. benchmarks in
their online programs on a scale of 1 to 3, where 1 is “Not present,” 2 is “Somewhat present,”
and 3 is “Present.” The average for the ratings within each factor was used to represent the
rating for the factor. The missing data was excluded from calculation. b
Table 8. The Distribution of the Perceived Presence
<1.5

1.5 – 2

2 - 2.5

2.5 – 3

6.3%
Less
likely
10.5%
Present
6.8%

17.0%
Slightly
22.0%
Present
22.3%

26.2%
Moderately
30.2%
Present
24.3%

50.5%
Present
37.3%

Teaching and learning

4.0%

17.2%

31.5%

47.3%

Assessment

8.9%

18.5%

34.2%

38.4%

Factor
Institutional support
Faculty support
Student support

46.6%

Table 8 shows the ratings for the presence of the factors. Half (50.5%) of the respondents
___________________________________________________________________________________________________________________
b

Faculty support had 12 missing responses (4%), institutional support had 6 (2%), and teaching
factor had 3 (1%). Assessment and student support had the highest missing responses, each with
15 (5%). Every factor’s missing data percentage was below 5%.
a 2.5 rating, the respondent needed to rate two indicators with 3.0 and two with 2.0. A score of

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gave the institutional support factor a rating of 2.5 - 3.0. For a factor with four indicators to have
2.5 and above indicated a presence for the factor. Overall the perceived presence was not as
strong as the perceived importance, and there was variation among the five factors. The faculty
support factor and the evaluation and assessment factor had the least presence, and the
institutional support factor had the highest presence. For the faculty support factor, there were
32% of the respondents who rated it lower than 2.0, which is an indication of the less strong
presence of this factor.
Presence and the level of satisfaction. The Kruskal-Wallis H test (also called the "oneway ANOVA on ranks") is a rank-based nonparametric test to determine if there are statistically
significant differences between groups of an independent variable. The Kruskal-Wallis test was
conducted to understand whether the presence of these factors (which represents the level of
practice of the quality indicators) differed by the satisfaction level for online teaching. The
dependent variable was the presence of each factor and the independent variable was the level of
satisfaction. The null hypothesis was that the presence of the factor was not different by the
satisfaction level of teaching online. Based on the results in Table 9, the null hypothesis can be
rejected (.05 significance). The presence of these five factors was associated with respondents’
satisfaction level for online teaching.
Table 9. The Association Between the Five Factors and the Satisfaction
Factor
Null
Sig.
Satisfaction level
Institutional support

Reject

0.000

Associated

Faculty support

Reject

0.000

Associated

Student support

Reject

0.027

Associated

Teaching and learning

Reject

0.000

Associated

Assessment

Reject

0.000

Associated

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The results of Kruskal-Wallis test show that the five factors did have association with the
level of satisfaction. The significance level was .05. Taking the faculty support factor as an
example, the detailed results of the Kruskal-Wallis test indicated that the presence of the faculty
support factor was positively associated with the satisfaction level of the online teaching
experience. As shown in Figure 5, the perceived presence of faculty support was positively
associated with satisfaction for the online teaching experience. In other words, the more these
quality indicators were being practiced, the higher the satisfaction level for teaching online was.
The other four factors had a similar positive association between the rating for presence of the
factor and the level of satisfaction level for online teaching experiences.
Figure 5. The Association between Faculty Support and Satisfaction

Presence and concern for quality. The ratings for the presence of these factors were
further examined, to understand whether the perceived presence of these factors was associated
with the level of concern for quality. The dependent variable was the factor and the independent
variable was the level of concern. The null hypothesis was that the presence of a factor did not
differ by the level of concern, which indicated that there was no association between the
presence of factor and the level of concern for quality.

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As shown in Table 10, the perceived presence of four of the five factors was associated
with the level of concern, with an .05 significance level. The teaching and learning factor was
not associated with the level of concern.
Table 10. The Association Between the Five Factors and the Concern for Quality
Factor

Null

Sig.

Concern for quality

Institutional support

Reject

0.000

Associated

Faculty support

Reject

0.000

Associated

Student support

Reject

0.017

Associated

Teaching and learning

Accept

0.384

Not associated

Assessment

Reject

0.000

Associated

Taking the institution support factor as an example, the detailed Kruskal-Wallis test
showed that the presence of institutional support was associated with the concern for quality. As
shown in Figure 6, the perceived presence of institutional support was negatively associated with
the level of concern about quality. In other words, the higher the presence of faculty support, the
less likely the faculty were concerned about quality.
Figure 6. The Association between Institutional Support and the Concern for Quality

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The Kruskal-Wallis tests were repeated for all five factors, with the results indicating that
the presence of these factors was mostly associated with the satisfaction level and the concern for
quality. Table 11 shows that the presence of all five factors was positively associated with the
respondents’ satisfaction for online teaching; the presence of four out of five factors was
negatively associated with the respondents’ concern about quality.
Table 11. The Summary of the Association
Perceived presence

Satisfaction level

Concern for quality

Institutional support

Positive association

Negative association

Faculty support

Positive association

Negative association

Student support

Positive association

Negative association

Teaching and learning

Positive association

Not associated

Assessment

Positive association

Negative association

Why was the teaching and learning factor not associated with the faculty’s concern for
quality? One possible reason is that teaching and learning are complex processes with which a
lot of possible quality indicators are involved. The current study only included four indicators
which may not have been enough to reflect diverse opinions of faculty. The number of quality
indicators included in this study was not enough to reflect faculty’s perceptions. Further study is
needed to focus on the teaching and learning aspects of online education, for which is possible to
include more quality indicators.
Section Summary
The results from the rating analysis show that these quality indicators were rated high on
importance. The results also show that the ratings of the presence of these indicators were
positively associated with faculty satisfaction from teaching online and negatively associated
with their concern for quality (with one exception, the teaching and learning factor). Both

62

findings confirmed that the quantitative data analysis suggests that the U.S. quality indicators for
online education are perceived as relevant by Chinese faculty. The next section explores the
perceived relevance from the open-ended questions.
4.2 Open-Ended Question Analysis and Findings
In addition to asking the respondents to rate the importance and the presence of the
quality indicator, the survey also included eight open-ended questions that were designed to
provide the respondents with the opportunity to express their opinions and suggestions on quality
assurance. These open-ended questions asked about the purposes of online education, the
characteristics of quality programs, the faculty’s satisfactions with the teaching experience, and
the relevance of the U.S.-based benchmarks to Chinese online programs. The detailed results are
shown in Appendix 8.
Data Preparation
Open-ended question responses were filtered before data analysis. For the open-ended
question responses, two hundred and one records were included and the criterion was to retain
faculty respondents’ responses. A comparison was made to ensure that there were no
meaningful information loses when the 201 cases were used. Because each question asked the
participants provide three answers, there were 343 to 493 responses for each question (Appendix
8).
Coding Process
The open-ended question analysis included several processes. All responses were first
read to gain a sense of the emerging themes. Whenever a meaningful theme appeared, the theme
was assigned a code and a description. For each question, there was a code table. Each response
was read carefully. Based on the interpretation of the response meaning, the response was

63

labeled with the appropriate code. After all responses were coded, 10% of the coding results
were randomly checked by an independent Chinese scholar, to ensure coding accuracy and to
avoid the researcher’s own biases affecting the data. Patterns and trends were identified as
findings.
How Do Participants View the U.S. Quality Standards?
Faculty responses and comments provided a deeper understanding of their opinion on
quality assurance. The results from the open-ended question analysis were presented by taking
faculty’s words that appeared in the responses and rearranging the words to tell the story. The
key points of the meanings derived from the themes from each open-ended question. The
presentation of the results is divided into two sections: The first is to present the findings related
to the Chinese faculty’s overall perception of the relevance of U.S. quality standards. Then a
more in depth investigation can be utilized to look at Chinese faculty responses relative to the
individual factors.
Figure 7: The Answers for Whether the Respondents Had Concern for Quality

Figure 7 shows an example of the open-ended question analysis process. In this openended question, the participants were asked “Is there any concern about the quality of online

64

education?” Twenty-seven percent respondents had no concern, 47% were a little concerned,
20% had some concerns, and 6% were concerned.
The respondents provided 339 responses for their reasoning on this question. These
responses were coded, and the aggregated results were organized in Table 12. The left column is
the themes derived from the responses; the middle two columns are the number and the
percentage of responses for each theme; and the right column is the example of responses.
Table 12: The Reasons for Having Concerns for Quality
Inductive themes

N

Example responses

Student motivation

86

25%

Some students did not put effort into learning.

Faculty quality

53

16%

Online teachers lack motivation.

Assessment

40

12%

It is difficult to know the learning results.

Content

37

11%

Course materials are not updated regularly.

Management

29

9%

Interaction

31

9%

Quality focus

26

8%

There is not enough monitoring and managing
process.
There should be more timely communication and
interaction between students and teachers.
The quality of online education cannot be trusted.

Policy issue

14

4%

Online environment needs to be regulated.

Acceptance

15

4%

Online education has not being widely accepted.

Other

8

2%

Miscellaneous or not making sense

The reasons for the Chinese faculty to have concerns for quality included: students are
not motivated, some faculty are not qualified to teach online, management and organization are
not adequate, and the assessment methods are not able to assess student learning. These reasons
for concern were more related to the outcomes of quality assurance, while the quality indicators
included in the study were elements and conditions that were inputs for quality assurance. The

65

responses from the open-ended question showed that Chinese faculty’s concern for quality was
not fully covered by the U.S. quality standards.
To understand more about Chinese faculty’s attitudes toward U.S. quality standards of
online education, two additional questions were asked: “Do these benchmarks reflect your
criteria for the quality of online education?” and “Should Chinese online programs adopt these
benchmarks?” The results illustrate the complexity of understanding and applying educational
standards across borders. Every participant in the study agreed that these U.S. benchmarks
reflected his/her criteria for quality.
Figure 8: The Answers for Whether the Indicators Reflect Criteria for Quality

The majority of respondents indicated that this set of U.S. quality indicators reflected
their criteria of quality (Figure 8). They commented that these indicators covered a wide range
of quality issues in online education. Some of them also regard this set of benchmarks as
practical and can be applied in practice. It is clear that the majority of respondents perceived this
set of benchmarks as beneficial. Faculty were also happy to know what areas the U.S. quality
standards cover and what details the U.S. quality indicators provide.
Should China adopt these U.S. quality standards? Every participant indicated that China
should at least partially adopt this set of U.S. quality benchmarks (Figure 9). They provided
reasons such as “China is at the beginning stage of online education.” “China needs to develop
66

quality standards in order to ensure quality.” Even though the Chinese government and
institutions had put forth a great deal of effort, the respondents felt that China is still far from
having quality standards that reflect the best online teaching practices. They argued that China
needs to learn from more advanced practices, including the quality standards from the U.S.
Figure 9: The Results for Whether China Should Adopt the U.S. Quality Indicators

Table 13: The Responses for China’s Adoption of the U.S. Quality Indicators
Inductive Themes

N

Example responses

China needs standards

119

China can learn from the U.S.

66

Useful or partially useful

65

34% China cannot reply on other countries'
standards; it should develop its own.
19% China can learn from US in quality
standards.
19% This set is very useful or partially useful.

Country difference

29

8%

Shared value

19

6%

Other

20

6%

Internationalization

17

5%

Benchmarks need more work

10

3%

There are differences between U.S. and
China.
China should adopt these Us benchmarks
because there are shared value for quality.
China needs to connect with international
standards.
This set of benchmarks needs more work.

In addition, the respondents emphasized the importance for China to connect with the
international community by adopting international standards. Some respondents argued that the

67

quality of online education has shared principles that are independent from the environment.
Overall the respondents made their argument for adopting these U.S. based quality indicators
(Table 13).
On the other hand, the respondents pointed out the difficulty of applying the U.S. quality
indicators to Chinese online education. Ten percent of the responses indicated that educational
standards are subjective to local matters. The respondents argued that the U.S. educational
environment is quite different than China. Because of the historical, cultural, political, and
economic differences that exist between China and the U.S., it was expected that education
standards should reflect such differences. Therefore, it is difficult to apply U.S. standards
directly in China and China cannot rely on other countries' standards; instead, China should
develop its own standards.
In addition, 13% of the responses indicated that there were limitations with these
benchmarks. Even though this set of benchmarks covered a lot of generic quality issues at the
program level, it does not include specific benchmarks to be applicable to specific disciplines.
The survey participants, being teachers, wanted to see more about benchmarks related to the
courses they were teaching. The results did not reveal variation in open-ended question
responses by demographics such as gender, experience of teaching online, and disciplines.
The conclusion is that these benchmarks were perceived by Chinese faculty as relevant.
The results suggest that the participants viewed the U.S. quality standards for online education as
very important to their work and to Chinese online education programs. The participants also
viewed that these U.S. quality standards need to be carefully studied and modified to suit the
Chinese educational environment.

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Findings Organized by Factors
The previous section presented the overall perception of Chinese faculty regarding the
relevance of the U.S. quality standards. This section presents detailed qualitative comments,
organized around five quality factors that were proposed in CFA. What the researcher was
looking for is whether the comments from Chinese faculty were in any way different from the
content of U.S. quality indicators. The results show that, in their own language, in their own
words, the responses to the open-ended questions reflected the content of the U.S. benchmarks.
That added validity to the conclusion that these U.S. benchmarks are relevant to China.
Findings Related to Institutional Support. Institutional support covers a wide range of
topics, including policies that contribute to the acceptance of online learning. Respondents were
very clear about the objectives of online education in China. According to the respondents,
Chinese online education programs are perceived as to build a lifelong learning environment; to
provide open access and flexible learning opportunities; and to ensure education equality by
widening channels for knowledge dissimilation.
Enabling anywhere and anytime teaching and learning is a major objective and
characteristic of Chinese online education. This point was reflected in 22% of the responses.
Flexibility is also a main reason for faculty members to enjoy their online teaching experiences.
The flexibility enables faculty to better manage time and to use various ways to teach. In
addition, the convenience of online learning was also considered by participants as a great
service to students.
Resource-wise, online education was considered by participants as having a major
advantage in comparison to face-to-face education. Some respondents felt that the online course

69

material can be repeatedly used and shared beyond institutional boundaries. Being able to share
educational resources was mentioned in 14% of the responses.
While online education was perceived as important and beneficial, online education is
still not widely accepted in China. Part of the reason for the low acceptance is the perceived low
quality of online education. Seventy-two percent of the respondents indicated having some
concerns about the quality of online education in China. They argued that online environment
needs to be better regulated to ensure quality. Some respondents also suggested that online
education in China needs more support from the government and better acceptance from society.
Another reason contributing to the low acceptance of online learning is the lack of
attention from the institution. Some respondents commented that their institution focused too
much on making money from online program; not enough on student learning. They argued that
the institution should pay more attention to developing policies that relate to academic quality
and integrity. One particular issue mentioned is the importance of teaching online, being
perceived by both faculty and the institution. Some respondents complained that the online
teaching experience was not considered as part of promotion evaluation.
Findings Related to Faculty Support. Faculty are one of the most important factors to
successful online education (Fuller, 2006). In order to effectively teach online, good quality
faculty members need to have knowledge about their field of teaching, need to have clear
teaching and learning objectives, need to have understand the technology, and need to be able to
teach in various ways to match with different student learning styles. This view of the
importance of faculty was agreed upon by the respondents in this study. In reality, these desired
attributes for online faculty are not easy to develop.

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Half of the respondents were concerned about online faculty quality. One of the reasons
for having such concern is that online faculty members have uneven qualifications. The
respondents argued that not all faculty are qualified to teach online. The current practice at the
institution is to identify the main instructor for the face-to-face course. If he/she agrees to teach
the same course online, he/she will be the lead faculty and will be in charge of designing and
teaching the course. The respondents felt there should be a more rigorous screening process
when hiring online faculty. The institution should provide incentives to attract more highly
qualified faculty to join online teaching. Motivating faculty in online teaching is easier said than
done, since the contribution to online teaching is not considered when faculty are evaluated
professionally.
In addition to the recruiting process, challenges also occur when faculty switch from
face-to-face teaching to online teaching. Forty-five percent of the respondents were teaching
face-to-to face versions of the course. Some faculty members were teaching online courses as if
they were teaching face-to-face courses. Many faculty members have an inadequate
understanding of technologies for online teaching. One suggestion was that faculty members
should be assisted during the transition from face-to-face teaching to online teaching, and the
school should regularly train online faculty, continuously develop faculty in terms of
understanding the online teaching pedagogy and technology. In terms of the areas for faculty
professional development, the respondents mentioned the need to know the best practices of
online teaching, online pedagogy, educational technology, and standards from other countries.
They also argued that online programs should require faculty to effectively utilize educational
technology.

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The respondents suggested that developing more responsible and competent faculty who
understand the purpose and pedagogy of online education should be a priority for institutions.
Many respondents welcomed the professional development opportunities. Getting more training
and professional development is not a burden to them. It is actually a motivation. Some
respondents perceived that online education is the future and they need to know more about it.
Getting involved in online courses is good for their career. As one participant responded,
“Teaching online helps improving my professional knowledge and course designing skills.”
The results show that Chinese faculty agreed that online faculty need to have proper
qualifications in order to be effective in online teaching. They also suggested that the institutions
should do more to recruit qualified faculty, and to provide more professional development
opportunities.
Findings Related to Student Support. Most students in ABC University’s online
programs are full time working adults. They have to balance pressure among study, work, and
family obligations. How to provide proper support to these students has been a focus for ABC
University. The support to students comes from a mixed structure of between 240 regional
student learning centers, online learning materials, faculty virtual office hours, and student
learning groups. According to the respondents, a good online program should recruit motivated
students who are self-disciplined to participate in online learning. Currently, students who are
enrolled in ABC University’s online program most likely did not pass the Chinese national
college entry exam. Some respondents felt that some students getting into online programs are
not well prepared for online learning.
But the biggest challenge for student support is about students’ lack of motivation.
Student motivation was mentioned by respondents as both a necessity and a challenge. Some

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respondents noticed that students’ attitude toward learning is questionable. They mentioned that
many online students focus not on learning, but on getting the diploma. Students lacking
motivation to succeed in online learning has negatively affected the learning results and the
quality of the degree. It has even negatively affected faculty’s satisfaction with teaching online.
Thirty-five percent of the respondents who said they had reservations about teaching online in
the future listed student lack of motivation as a main reason. The respondents suggested that the
institution should have more effective student support structure, including improving the
orientation process prior to taking online classes and providing more guidance for students to
navigate the learning once they are in the program. They also suggested that the online program
needs to be more selective during admission.
The results showed that supporting online students is an important task. One of the most
challenging tasks is to motivate online students. Chapter Five discusses more on what is needed
to motivate online students.
Findings Related to Teaching and Learning. Teaching and learning are at the core of
online education. Many respondents enjoyed their online teaching experiences because online
teaching provides easy and flexible access, enables rich teaching styles, and individualized
learning. In order to have quality online teaching, the respondents believed that the courses
should be designed so that students can engage themselves in analyzing and evaluating their own
learning process and faculty should understand the content, the online teaching pedagogy, and
the technology. There were two major themes that emerged from the responses: interaction and
technology use. Both themes are critical to the success of online teaching and learning.
Interaction was mentioned as a key issue for online teaching and learning. It is also a
major concern for many respondents. Without adequate interaction, it is difficult to know how

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students are learning. Some faculty members felt they did not get to know students enough. One
respondent answered, “Unlike my face-to-face teaching, I do not know my online students. I do
not know their learning style, their struggles, and their thinking. It is very challenging and
unrewarding when a teacher does not know students at the end of the semester.”
Technology know-how and the proper use of technology in online teaching is another
issue. In the face-to-face format, faculty can talk to students and see their reactions. In online
education, technologies become the principal means of communication, which is not the case in a
classroom. When the technology is not available, or the instructor does not know how to use the
technology, or uses the technology improperly, the effectiveness of online teaching and learning
can be greatly handicapped.
Findings Related to Assessment. To the respondents, a good online program needs to
systematically evaluate the processes and the results. The evaluating process needs to be
standardized and properly managed. In practice, many respondents acknowledged that too often
they do not know how much students learn and cannot provide timely feedback to students. The
respondents suggested there should be multiple ways to evaluate student learning.
The current assessment method at the ABC University relies heavily on the final exam,
which is required for every online class. The final exam at ABC University is a very formal
process. During the final exam week, online students go to regional learning centers around the
country to take the final exam in person, for every course they are taking. The institution sends
staff members to different locations to monitor the exam process, in order to ensure that the
procedures are followed. However, the final exam happens once per semester. If faculty
members relied on final exams to gauge student learning, they would not have much success. By

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the time the results of final exam are available, the class is probably finished. There is not much
for the teacher to do to help students.
Inadequate evaluation structure and methods was mentioned as one major issue at the
ABC University. To ensure teaching quality, the Online College at ABC University has
developed procedures to monitor teaching activities. This process involves a data collection
process that covers seven categories, at 53 check points. The data collection includes how fast
the teacher responds to student questions, the time a teacher spends in the virtual office, the
number of posts a student makes in the discussion forum, etc. The results from the evaluation
are directly linked to the financial payment to faculty members. The teaching and leaning could
be even more effective at the ABC University if the data is carefully analyzed and the results are
used for improving teaching and learning processes. What is needed is a deeper understanding
of the purpose for assessment and evaluation and being able to use the evaluation results to guide
the teaching process. Some respondents suggested that there should also be evaluation of how
that virtual office time is used.
Finally, the authenticity of student work was questioned by the respondents. As one
respondent answered, “How do I know the homework was actually finished by the student
himself?” Currently, there is not a good way to verify students’ identity and their work. It is
even more difficult when plagiarism is not clearly defined or understood. For example, cheating
during the final exam is well understood by Chinese students as plagiarism. But using other’s
work without proper citation may not be considered so.
Summary of the Open-Ended Question Findings
The analysis above used comments from the responses to the open-ended questions to
build a story about Chinese faculty’s perceptions of quality status and quality assurance at ABC

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University. The results show that the quality indicators from the U.S. are relevant to China,
evidenced by respondents’ concerns, desires, and suggestions through their comments. What the
open-ended question responses confirmed was that the Chinese faculty had similar concerns for
the quality of online education. They viewed that these U.S. quality indicators reflected their
own criteria for the quality of online education. They suggested that China should at least
partially adopt these quality standards. But they also strongly argued that these foreign quality
indicators should be modified to fit Chinese online educational settings because of the
differences existing between the two countries.
4.4 Post Hoc Analysis
The previous section was built around the five quality factors that were based on the U.S.
quality literatures. The quantitative and qualitative results confirmed that these five quality
factors were perceived as important and relevant by Chinese faculty. It is time to step back and
revisit the data and see what else might stand out.
Management Factor
A five factor categorization has been proposed so far as a framework to study online
education quality in China. These five factors are institutional support, faculty support, student
support, teaching and learning, and assessment. There is another factor that emerged from the
open-ended question responses, the “management” factor. According to respondents, the
“management” factor is an important part of the quality assurance. They mentioned that a
quality program should have a systematic management procedure and a high efficiency
management structure and team. The respondents were concerned when management was
lacking. They were especially concerned about not enough monitoring and managing process.

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In order to improve the quality of online education, the respondents viewed developing
effective managerial procedures as a very important step to improve the management of online
teaching and learning. They also suggested enhancing the management function in the teaching
platform and improving the management function within academic units. Chapter Five discusses
more about what the “management” factor means to the model of online education quality
proposed in the study and what might be the cause for this factor to be perceived as very
important, even though it has not been mentioned widely in the U.S. literature.
Reducing the Number of Factors
In the CFA, two factors were removed because of their strong association with other
factors. The technology factor was combined with the institutional factor and the course
development factor was combined with the teaching and learning factor. The result was a fivefactor categorization that fits with the data. Then the question was can these five factors be
analyzed again to see whether their number can be further reduced? Will a further factor
reduction provide more understanding of the quality assurance issue in online education?
Table 14. Further Factor Analysis Results
Factor
1

2

Institutional support

.328

.684

Faculty support

.285

.665

Student support

.600

.535

Teaching and learning

.669

.375

Assessment

.789

.291

For this purpose, factor analysis was conducted to explore whether more factors could be
reduced. The factor reduction results show that these five factors can be clustered into two

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groups, as shown in Table 14. One group has the institutional support factor and the faculty
support factor. The other group has three factors. Chapter Five discusses more about what these
two groups mean and how to go about understanding online education program quality using this
two-factor approach.
4.5 Chapter Summary
This chapter first presented the CFA findings of the model fit with the quality of online
education. The results show that the data did not fit well with the proposed seven-factor
categorization, which was then modified by removing some indicators and re-organizing others.
In addition, the seven factors were reduced to five. The result was a five-factor categorization
with 19 indicators. Each factor included three to four quality indicators. The average of these
indicators’ ratings was used to represent the rating for the factor. These ratings for factors were
used for further analysis.
The results show that respondents perceived these U.S. based benchmarks as very
important, but the level of presence of these indicators varied. The perceived presence of these
benchmarks was positively associated with faculty’s satisfaction with online teaching, and
negatively associated with their concern for quality (with one exception: the teaching and
learning rating did not show an association with the concern for quality).
Responses to open-ended questions were also analyzed to understand faculty beliefs,
concerns, and suggestions related to the quality of online education in China. The results show
that Chinese online faculty agreed that China needs workable quality standards for online
education. China needs to develop quality standards. During the process of developing such
standards, China should learn about and maybe borrow other good practices, including the
quality standards used in the U.S. The respondents also recognized the internationalization trend

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within higher education. They regarded adopting standards from other countries as an important
step for Chinese institutions to connect to the international community.
On the other hand, the respondents perceived the process of adopting international quality
standards as a complex process due to educational setting differences. The differences that exist
between China and the U.S. are tremendous. The respondents cautioned that, even with their
great value, the U.S. standards cannot be blindly copied and applied in China. Instead, China
should learn from and borrow the U.S. standards, and modify them to reflect the Chinese
educational environment. Ultimately, China should develop a set of quality standards that works
for China.
The findings from the study also shed light on the details of the quality issue in China.
Before this study, concerns about online education quality were often without details. This study
provides more specifics about the quality issues of online education from the faculty perspective.
The description of the problems and their causes can be useful to address the quality problem.
Chapter Five discusses the meaning of the findings and the practical implications for online
education and quality standard development in China.

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CHAPTER 5
Discussion
5.1 Summary of the Study
Online education has the potential to help address the higher education access problem.
Online education has become a major component of higher education (Jung et al., 2011). The
quality of online education, however, has been a major concern for stakeholders. In order to
ensure quality, quality standards are necessary and need to be understood for the appropriateness
and applicability of online higher education.
This study aimed to understand whether the U.S. quality standards for online education
are relevant to the Chinese educational environment. Nine sources of U.S. literature within the
field of online education were identified. The U.S. literature included quality standards from
major U.S. accreditation agencies and research publications. The analysis of these U.S. quality
standards led to 31 quality indicators that were categorized into seven factors. A survey was
administered at a Chinese institution, and the participants were asked to rate the importance and
the presence of these quality indicators. Three hundred seven valid responses were collected and
statistically analyzed. Two hundred one responses were used for open-ended question analysis.
The following sections discuss the relevance of the U.S. quality standards to China from four
points of view.
Are the U.S. Quality Standards Perceived as Relevant?
The ratings. Chinese faculty were asked to rate the importance and the presence of these
indicators. The results show a homogeneous high rating on importance, indicating that the U.S.
quality standards are highly regarded by Chinese faculty. It is reasonable to believe that these
U.S. benchmarks are considered important and valuable by Chinese faculty. The ratings also

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indicate that the majority of these U.S. based indicators are present in this Chinese institution,
even though the degree of the existence varies. This finding suggests that the Chinese are
already practicing many of the quality indicators themselves. In addition, the presence of these
U.S. indicators was found to be positively associated with participants’ satisfaction with online
teaching, and negatively associated with their concern for quality. This implies that the more
these quality indicators are practiced, the happier the faculty will be, and the less concern they
may have for quality. This finding confirms the perceived relevance of these quality indicators.
The open-ended questions. The results from the open-ended questions analysis further
add to the validity of the claim that the U.S. quality indicators are perceived as relevant. Take
the comments about the faculty support as an example. The respondents perceived that faculty
members need to have knowledge about their field of teaching and the institution should provide
incentives to attract more highly qualified faculty to join online teaching. In addition, the school
should regularly train online faculty, and continuously develop them to understand better online
pedagogy and its technology. In their own language, their own words, what the respondents are
saying about faculty support reflects the U.S. quality indicators’ content. Similar patterns can be
found within the comments on the other four factors. This finding adds validity to the relevance
of the U.S. quality standards to Chinese online education.
Criteria of quality. When asked whether the U.S. quality indicators reflected their
criteria of quality, half of the respondents answered totally reflected or reflected. The other half
answered reflected some. The detailed comments show that the difference between the answers
from these two groups was not in the content of these quality indicators, but in the context in
which they will be applied. Many respondents think that educational standards should reflect the

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environment differences. According to the respondents, because China is very different than the
U.S., this set of benchmarks needs to be modified and customized to suit Chinese environment.
In summary, the findings suggest that the U.S. quality indicators are perceived by
Chinese faculty as important, and that Chinese faculty have similar concerns about quality to
those that were appeared in the U.S. literature. Most respondents also agreed that China should
at least partially adopt these quality indicators and integrate them into Chinese online education
quality assurance practices. At the same time, the respondents cautioned that the adoption of
foreign standards can be a complicated process. The premise is that while the U.S. quality
standards are perceived by be faculty as relevant and applicable to the Chinese context, these
indicators have not adequately covered all aspects of quality for online education. In order to
successfully adopt these indicators in China, they need to be modified to fit with the current
Chinese online education environment. The remainder of the chapter explores this premise fully
by discussion of: 1) whether the U.S. quality indicators adequately covered the quality issue for
online education; 2) how to go about doing the modifications necessary for U.S. quality
indicators to work in Chinese settings; 3) how this study relates to other quality studies of online
education; and 4) what the implications of the study are for quality assurance practice and
research.
5.2 Are the U.S. Quality Standard for Online Education Adequate?
This study went through methodological procedures to have an argument for identifying
quality indicators in which the online quality is perceived in the U.S. These quality indicators
were then tried in a Chinese institution. The results indicated that the Chinese faculty perceived
these U.S. quality indicators as important, and that these quality indicators have been practiced in

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China. There was overlap of the quality indicators used by China and the U.S. This implies that
the U.S. based quality indicators for online education can be shared across borders.
Measuring Quality - Inputs vs. Outcomes
The analysis of open-ended question responses revealed more findings and raised more
questions (Table 15). For example, when respondents were asked about the characteristics of
quality for online education programs, the respondents mentioned expanding access (28%
responses), shared resources and reduced costs (18%), having qualified faculty (12%), and good
management (8%). These were aspects not mentioned in the indicator pool included in this study.
Table 15. The Perceived Characteristic for Online Programs with Quality
Inductive Themes

N

Participant Responses

Expand access

155

28% Provide access and flexible learning opportunities.

Share resources

99

18% Have adequate resources and utilize resources efficiently.

High quality faculty

63

12% Have responsible and competent faculty.

Good management

42

8%

Have systematic process to evaluate teaching.

Create equality

38

7%

Open educational opportunities to variety of students.

Good interaction

32

6%

There is interaction between teacher and students.

Motivated students

32

6%

Students have the motivation to online learning.

Most of the quality indicators in the U.S. pool included in this study were conditions and
elements that are necessary for an online program to have quality. They are indicators being
considered as inputs. They do not measure quality from the perspective of outcomes. What
were mentioned by the Chinese, however, were indicators related to outcomes. If the effort of
quality assurance focuses on inputs but ignore outcomes, then we are missing half of the battle to
improve quality. This finding suggests the incompleteness of the U.S. Quality Standards. While
the U.S. quality indicators were perceived as important and relevant by the Chinese respondents,

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responses from the open-ended questions indicated that there are aspects that are not covered by
the indicators included in the pool. Two examples are online student motivation and online
program management.
The Incompleteness of the U.S. Quality Standards
Student motivation has been mentioned many times in the study as an important quality
indicator. The open-ended question responses provided such a view as perceived by the Chinese
faculty: a quality online education program needs to have motivated students. But the current
state is that online students are not motivated to learn, which has become one of the major
quality concerns for faculty. To help motivate students, there should be systematic efforts from
institutions to motivate online students. But the U.S. quality standards did not provide quality
indicators in this regard. Once again, the U.S. indicators addressed the inputs measure, such as
there is an orientation; students have access to services; and the institution has a structure to
address student complains. To measure student motivation, which is an outcome measure,
requires a different way of thinking about quality and a different set of indicators.
Management is another example that was missing from the U.S. quality indicators.
Managing an online program is a complicated undertaking. It involves many tasks that cover a
wide range of issues, including curriculum, staffing faculty, program outcomes, technology, and
institutional relationships (Kearsley, 2012). The results from the post hoc analysis show that
“management” was repeatedly mentioned by Chinese faculty as necessary to ensure the quality
of online education. Why has the management factor not appeared frequently in the U.S.
literature? Why did the SLOAN-C, as a major promoter of online learning, only recently begin
the discussion of efficiency and management within the online education community?

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One possible explanation is that the management issue becomes more prominent when
online programs increase in scale. In the U.S., online programs are usually developed based on
existing academic programs. The online programs in the U.S. are often small scale. They are
often managed by the institution’s academic units instead of a special unit on campus; the class
size is often small so that teachers can provide one-on-one assistance to students. When an
online program is small, management is less of an issue. But when an online program increases
in scale to the level similar to the ABC University, which has 75,000 online students, and half of
the classes have more than 100 students, management and efficiency issues become more
prominent. Even delivering exam materials becomes an organizational challenge, considering
the 240 locations around the country for the administration of final exams. In a large scale
online program, teaching and learning are important, but the administration must also ensure that
money, personnel, and time are managed so that courses are produced on time and many work
tasks fit together (Moore & Kearsley, 2011, p. 18).
Another possible reason for the management factor being repeatedly mentioned by
Chinese faculty is in the structure of the organization. At ABC University, there is a two-tier
organizational structure, where the Online College is responsible for all aspect of administrative
tasks, and the other colleges within the university are responsible for teaching tasks. There is a
lot of coordination involved. For this reason, each college designated a liaison to oversee the
online teaching from an academic perspective. Externally, there are 242 learning centers around
the country, and some are thousands miles away. Who is in control? What responsibility does
each party have and how do they account for the actions when involving separate organizations?
These two examples suggest that the U.S. quality standards are not perfect. The study
indicated even if Chinese online programs did adopt these indicators, the Chinese faculty will

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still have concerns on quality because these indicators do not cover all aspect of quality. The
findings indicated that the original quality model from the U.S. has shortcomings. If the goal is
to know whether online programs have quality, it is necessary to know more than what are
described in the U.S. standards.
What Does this Mean to the U.S.?
From the open-ended questions, the researcher identified some themes that were not
included in the original U.S. based literature. This implies that the issue of quality for online
education is indeed complex. Even the much researched U.S. quality standards do not cover
everything. The conventional ways of thinking about quality, represented in this study by U.S.
quality indicators did not fully capture the whole aspects of quality. This is consistent with
views of quality of academic programs that are solely using the inputs, not outcomes. There is a
need to re-think the quality of online education and the completeness of the U.S quality standards.
How do the findings from this study inform the conversation of online education quality in the
U.S.? Should the quality standards also include outcome measurement? The answers to these
questions bring us back to the conversation of quality, which was raised at the beginning of the
dissertation.
What Does this Mean to China?
Based on the results from this study, even if the Chinese replicate these indicators, the
quality assurance process is not necessarily going to deal with the concerns that came out of the
open-ended questions. In order for China to effectively adopt the U.S. quality standards, the
respondents from this study suggested that there is something beyond the indicator pool that
should be included in their quality assurance practice. The U.S. quality indicators need
considerable modification to suit the Chinese local settings. This modification process uncovers

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quality indicators that are unique to Chinese stakeholders and Chinese settings. It also includes
details that can be used as the base for action plan in China.
5.3 The Modifications Needed to Adopt the U.S. Quality Indicators
In order for China to effectively adopt the U.S. quality standards, the U.S. quality indictor
pool needs considerable modification. This modification process can be complex, because it is
influenced by stakeholders, policy, organizational structure, and the resources available. What in
particular needs to be modified in the U.S. indicators? Can it be assumed that this modification
process also applies to other international settings? The following sections discuss what
modifications are needed and how this modification process might be applied across borders.
There are two main areas that need attention: the modification to reflect local needs, and the
modification to provide the details needed for implementation purposes.
Modifications to Expand the Coverage of Indicators
Most of the quality indicators extracted from the U.S. quality standards for online
education are inputs by nature. These are the elements and conditions that are traditionally
considered as necessary in order for online programs to have quality. But the conditions and prerequisites will not necessarily guarantee good results. From the open-ended questions responses,
what emerged from the results is that Chinese faculty also care about the outcomes of quality
assurance. For example, the respondents prefer to see online programs achieve expanded access,
shared resources, high quality faculty, good interaction between teacher and students, and good
management. This implies that additional indicators are needed to measure outcomes.
The results also indicated that additional indicators are needed to reflect local needs. The
study so far has compiled a set of U.S. quality indicators and verified their relevance to Chinese
faculty. The results of the open-ended questions indicate that there might be issues and areas

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that are perceived as important by the Chinese, but were not included in the indicator pool. For
example, the management-related indicators were not included in this study when the survey was
developed. But the responses to the open-ended questions indicated a strong argument from
Chinese faculty that management is very important to the quality of online education in China.
There is also the need to modify the quality indicator pool to include additional indicators
that cover the management aspects of online education. Another example relates to learning
centers. The ABC University has 240 learning centers located in various regions of China to
provide student support. There was no quality indicator included in the pool to represent U.S.
quality assurance for these centers, because most online programs in U.S. do not have regional
learning centers to provide student support, as ABC University does. A modified quality
standard should include such indicators to cover learning center quality issues.
Additional indicators are also needed to reflect different perspectives of quality. In this
study, the faculty was the only stakeholder group surveyed. But quality assurance also matters to,
and is influenced by, other stakeholders, including the local educational authority, the
institutional administration, the staff, the employers, and the students. Different stakeholders
may bring different foci and opinions in terms of what should be included as quality indicators
(Twigg, 2001). The indicator pool should reflect such opinion differences.
Figure 10: Additional Coverage of Quality Indicators to Reflection Different Perspectives

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Figure 10 shows the concept of modified quality standards that include indicators unique
to difference stakeholders. The center circle is the selection of the U.S. quality indicators for
online education, agreed-upon by various groups of stakeholders as important and that should be
adopted. In the outer ovals, there are unique indicators that are perceived by different groups of
stakeholders as important.
Modification to Provide the Details as Action Plans
Having quality indicators is not enough. The purpose of having quality indicators is to
apply them for quality assurance purposes. The indicators included in the survey so far have no
details that can be used for implementation purposes. In practice, each quality indicator should
be carefully examined and modified for its applicability because of contextual differences. There
are many indicators, and each one of them requires attention to detail. The process of providing
the details for each quality indicator is where the majority of the modification work needs to be.
Take the course revision indicator as an example. The indicator states that instructional
materials, course syllabi, and learning outcomes are reviewed periodically. In order to apply this
indicator in practice, several questions need to be answered: How do faculty members go about
course material revision? How often should changes being made? How much change is needed?
Where will the resources come from? And when should the content be revised? Another
example is an indicator from the teaching and learning category. The indicator states that online
course design provides opportunities for appropriate instructor-student, student-student, and
student-content interaction. In order to apply this indicator in practice, there is a need to know
what kinds of interaction are appropriate, which may differ from course to course. How much
interaction is needed, based on the differences, by instructor and students? How to measure

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interaction based on the organizational structure? In particular, there are several key aspects
which need attention during this process.
Modification is needed to reflected differences in terminology. There might be indicators
with similar names that actually end up being quite different because of contextual differences
(Levin, 1997). Even with the same terminology, the actual meaning and the approach to
implementation can be different between the U.S. and China. This was also mentioned by
Chinese administrators from the ABC University, when the researcher presented the initial
findings from this study. Further work is needed in order to understand the differences and to
work out a strategy to reflect such differences.
Modification is needed to reflect the differences in actions. Given the same quality
indicator, when different stakeholders are asked about the action needed for implementation, the
answers can vary. This is because the interpretation of the meaning of indicators, in terms of
what is needed, can be influenced by a stakeholder’s background, interests, and experiences in
online education. Take one student support indicator as an example. The indicator states,
“Tutoring is available as a learning resource for students.” The answer to what should be done
depends on the students age, their readiness for online learning, their personal interests, learning
styles, experiences, and life circumstances. The action plan for students can be quite different
than those for teachers and administrators. One way to develop action plans is to have separate
plans for each stakeholder group, with details that are specific to the group (Schmidt et al., 2009,
p. 15).
Figure 11 illustrates such an abstract action plan. The plan should consider the local
needs and reflect the opinion differences from each stakeholder group. On the left is the quality

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indicator that is agreed upon by Chinese stakeholders. On the right is the implementation for
each stakeholder group. There should be a plan for each quality indicator’s implementation.
Figure 11. The Implementation Plan for Different Stakeholder Groups

What Would International Quality Standards Look Like?
Reviewing the methods utilized in this study, the researcher found that the processes of
selecting quality indicators is not limited to the U.S. The same processes can be used to select
European quality standards, and they can be applied to other countries. Similarly, the processes
to design the survey, to analyze the results, and to modify the indicators for implantation, are not
limited to China. It is reasonable to expect that the methods utilized in this study are country
independent and can be used across borders.
This study tells us that it is possible to have a shared internationally applicable standard
for online education. This shared quality standard has two parts. One part is applicable to all
educational settings. The indicators in this part are tested and agreed upon by stakeholders from
all countries. The other part is uniquely applicable to local settings. Quality indicators in this
part take into consideration local needs. This means that the additional indictors reflect local
needs and stakeholders’ unique perspectives, and they respect the history and culture of
educational practices. Figure 12 shows that a shared international quality standard includes the

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indicators that are agreed to by stakeholders from all countries, and leaves room for a diversified
portion of indicators that are specific to local settings. The modification process is to identify the
needs that are unique to the local settings, and to work out the details that reflect such needs.
Figure 12. International Quality Standard for Online Education

5.4 The Connection between This Study and Other Studies
The CFA results show that the five-factor model fits reasonably well with the data. The
five-factor categorization has been used for the analysis for this study. How does this model
compare with other quality models from the literature? Does this model fill any knowledge gap?
From four points of view, the following sections discuss the characteristics of this model and
how it connects with other models in the field of online education.
The Categorization Empirically Verified
Many studies have explored how to ensure the quality of online education programs.
Many publications have identified quality indicators and have proposed standards with
categorizations. Few studies, however, have taken steps to examine whether such
categorizations make sense.
Quality on the Line was one study that did ask the respondents to rate the importance and
the presence of the quality indicators. The result was a model that had quality indicators divided
into seven factors. But researchers from Quality on the Line made no effort to apply statistical

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test to ascertain the degree of importance of a benchmark. Instead, the study’s interviews guided
the analysis (Phipps & Merisotis, 2000, p. 13). Similar to Quality on the Line, this study also
asked the respondents to rate the importance and the presence of these quality indicators. This
study took the step beyond the presentation of the ratings by using CFA to build a five-factor
model. The result is a model that originated from the U.S. literature and was confirmed by
Chinese faculty responses.
Table 16: The Rating Comparison of Perceived Importance

Factor

This Study

Quality on the

<2.5
2.5 - 3
3 - 3.5
3.5 – 4.0
Less
Slightly Moderately
Very
Important Important Important Important

Line
Mean score
On a scale of 5.0

Institutional support

4%

13%

27%

56%

4.40

Faculty support

6%

18%

29%

46%

4.38

Student support

5%

23%

13%

59%

4.60

Teaching and learning

5%

17%

25%

54%

4.30

Assessment

5%

17%

27%

52%

4.36

Shown in Table 16, the results from this study were also compared with the findings from
Quality on the Line (Phipps & Merisotis, 2000). The comparison results indicated that there is a
strong similarity on the rating of the importance of these quality indicators between the Chinese
and American participants. Both studies show high ratings, which indicate approval from
participants. The results confirmed that what is perceived as important by U.S. scholars and
stakeholders is also perceived as important by Chinese online faculty.
Inter-Relationships among Factors
Many scholars have proposed online education quality indicators; many publications
have described quality indicators and grouped them into factors. But very few studies have

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mentioned the inter-relationships between these factors. Michael Moore is one of the few who
did suggest the inter-relationships between these quality factors (Moore & Kearsley, 2011).
This study demonstrated the existence of such an inter-relationship. Figure 13 shows that
statistically there is an inter-relationship between the quality factors. The correlations between
these factors ranged from .57 to .80, which implies that these quality factors were related to each
other. This finding confirms Michael Moore’s suggestion that there is interdependence of
subsystems in a distance education system. It also explains why some quality indicators look as
if they belong to more than one factor. Future studies could explore more what these
relationships mean and how one factor might affect other factors.
Figure 13. Correlations for Five Factors

Further Factor Reduction
The post-hoc analysis showed that the five factors can be further grouped into two broad
categories. This suggests that when faculty rated the importance of the indicators, their answers

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followed this two-cluster pattern. These two clusters can be viewed as what is happening before
and after the online classes (Appendix 9). The institutional support and faculty support factors
can be grouped into one cluster, which mainly happens before the online classes. The student
support factor, the teaching and learning factor, and the evaluation and assessment factor can be
grouped into another cluster, which happens during and after the online classes.
By dividing the quality factors into these two groups based on their timeline, online
education can be interpreted as follows: quality assurance is perceived by the Chinese faculty as
a process, which happens in two stages. One stage happens prior to the online classes; the other
happens during and after the online classes. For example, institutional support can influence the
environment for online teaching and learning; and faculty support can affect the faculty’s
motivation to get involved in online teaching. Both factors are related to what happens prior to
the online classes. The other three factors happen during the online classes or after the classes
(Figure 14). This two stage process reflects the reality of quality teaching and learning.
Figure 14. Five Factors Further Divided into Two Groups

This finding provides validity for the survey responses. When the respondents rated the
importance of these quality indicators, they were answering the survey questions based on their

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thinking as a teacher. If they were answering the questions arbitrarily, it would be difficult to
find such a cluster relationship among factors that also reflects the reality. The fact that these
five factors can be grouped into two clusters that reflect the reality provides evidence that the
survey results are valid. In addition, this finding provides a different perspective from which one
might view and implement quality assurance from a staged approach.
In summary, this study took a modeling approach to explore quality standards
applicability cross borders, by testing U.S. quality indicators in China. The modeling approach
uniquely confirmed the quality indicators’ categorization of the data. It fills the knowledge gap
by exploring the legitimacy of such categorization. The approach also provides an example to
decide what quality indicators should be included, based on the stakeholders’ opinions. The
modeling results also demonstrated the inter-relationships between quality factors and the need
to explore further such relationships. This finding points to the future research direction which
requires more attention to the details of such relationships. Lastly, the five factors could be
further grouped into two clusters, which suggest a new approach to study the quality of online
education, in which quality assurance is considered as a two-stage process.
5.5 Implications
The findings from this study could potentially help Chinese institutions improve the
quality of online education. The following sections discuss the implications for the practices of
quality assurance for online education in China.
Implications for Institution Policy
Online education not being widely accepted in China is a major concern for many
respondents. There are two reasons that might contribute to the acceptance issue. One is the
institutional policy that did not encourage faculty to participate and excel in online teaching.

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Currently, faculty at ABC University are provided with financial compensation to teach online
courses at ABC University. But their online teaching experiences are not considered as their
normal workload, but as overload. The online teaching experience and the performance of
online teaching are not associated with faculty promotions. Under the current policy, the
contribution faculty members make to online teaching are not related to their professional
improvement.
The results from this study show that this policy negatively affects faculty motivation to
teach online courses and to excel in online teaching. Should the online teaching experience be
considered as part of faculty evaluation? The main academic values about faculty work lie in the
promotion decision where faculty seek clues about the value of their work (Fairweather, 2002).
In order for faculty to engage in best practices for online teaching and to improve continuously
their knowledge, the best avenue for the ABC University is to include online teaching
experiences in the consideration of faculty promotion. The respondents suggested changing the
institutional policy to include online teaching in faculty evaluation and promotion considerations.
Of course, additional studies are needed to understand fully what motivates ABC University’s
faculty to participate and to excel in online teaching.
The other reason for the low acceptance is the perceived lower academic status of online
learning. This is partly due to the separation between online education programs and the face-toface academic programs. Unlike the common practice in the U.S., where most institutions
include online courses as the regular course offerings, online courses at ABC University have not
been available to on-campus students. This separation has created a barrier for faculty to
understand fully online teaching and learning. ABC University should consider integrating
online courses into its regular on-campus course offerings. By doing so, faculty and students

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who have not been involved in online teaching and learning could have a better understanding
and appreciation for online learning. The addendum discusses a recent change at ABC
University that has reflected a policy change.
Implications for Faculty Support
Teacher quality is the single most important variable influencing student achievement
(OECD, 2005). This study confirmed such a belief. The results show that teacher quality is also
one major concern that needs improvement. Professional development has been mentioned as a
critical component to improve teacher quality. This is especially true for faculty who are new to
online education, in order to learn and practice online pedagogy (Moore & Kearsley, 2011).
What kind of professional development will online faculty need? These were mentioned
by the respondents: “best practices of online teaching,” “online pedagogy,” “educational
technology,” and “standards from other countries.” In essence, online faculty need to have the
knowledge of the field, know how to teach online courses using proper pedagogy, and effectively
utilize educational technology. This suggestion aligns with other scholars’ opinion that online
instructors need to have Technological Pedagogical Content Knowledge (TPACK), in order to be
effective in online teaching (Mishra & Koehler, 2006).
However, the integration of TPACK in a multi-faceted and ill-structured online
environment is a complex process. There is no single framework can tell the complete story and
provide all the answers (Mishra & Koehler, 2006). Faculty have to develop continuously their
skills, learn about and practice theoretical frameworks, and eventually develop their own style of
online teaching. Participating in professional development is one way to gain such knowledge
and skills and online faculty at ABC University welcome professional development opportunities.

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Implications for Student Support
Students lacking motivation has been mentioned by many respondents as one major
obstacle to improving quality. How should one motivate online students? This is probably one
of the most challenging tasks facing Chinese online education. With the large number of online
courses offered, this issue becomes even more critical. Online students have special needs in
comparison to on-campus college students. These special needs have to be identified and
understood. According to Workman and Stenard, the first need of online students is the
consistency and clarity of online programs, policies, and procedures. For online students to be
successful in their online learning, they need to have a good understanding of the school and the
program. The second need is self-esteem, which online students can build from nurturing
interactions with faculty. The third need is to connect with the institution and to be socially
integrated with peer students, staff, and faculty members. The fourth need is the availability of
and the access to institutional support services (Workman & Stenard, 1996).
The results from this study suggest that there has not been a good method to address the
student motivation issue. Current student data collection at ABC University did not include
adult students’ emotional and spiritual dimensions, as well as their work-related dimensions
(Cleveland-Innes & Campbell, 2012; Dirkx, 2011). Student support service has not taken into
consideration each student’s unique learning style, their ability to learn, and their available time
to spend in online learning. It is clear that more research work is needed to understand Chinese
adult students’ special needs in order better to support them. What ABC University needs to do
is to develop a framework, based on theory, to guide student data collection and student support
programs. The data collected, analyzed, and used has to be based on the understanding of
student characteristics, skills, and their internal and external motivation factors. In practice, a

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student support profile database can be useful. This student support profile includes information
about each student’s needs and learning style. When needed, students, instructors, local learning
center staff, and management can access a student’s profile online. By checking the profile, the
instructor can know more about students’ leaning progress, and their struggle from the previous
classes, and can be able to provide appropriate help the students may need.
Implications for Teaching and Learning
One key issue in online teaching and learning, agreed upon by both Chinese and
American scholars, is the lack of interaction. The respondents from this study suggested that
better interaction is needed in the online format of teaching and learning. The nature and the
extent of the interaction needed in online learning is based on elements such as organization,
teaching philosophy, the nature of the subject matter, the students, and the technology used
(Moore, 2012). There is not one fixed solution to provide good interaction. There are several
things ABC University can consider. The first and the foremost is a clear understanding of what
interaction is needed in ABC University’s online program. For example, there are different ways
for faculty to interact with students, including email, discussion forums, virtual office hours,
video conferencing, web chats, and telephone calls. There should be data to support what format
is required and how much is being used.
The second is the results of interaction. As faculty in the study pointed out, having
interaction does not mean the interaction is effective. For example, when a student posts on the
discussion forum, interaction happens. But it is not good interaction if the student does not read
others’ posts and provide comments. Another example is the required virtual office hours.
Online faculty at ABC University are required to be in the online virtual office each week for
certain time slots. The plan is that students can potentially interact with the faculty. But having

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the faculty available does not mean students will come to the virtual office and use the time to
ask questions. Even though faculty spend time in the virtual office, if no students come to the
virtual office to ask questions, it is not good interaction.
The third aspect of improving interaction is about the role technology plays. Because of
the many kinds of technologies available, it is a challenge for faculty members to be aware of the
availability of technology, to know how to use it properly, and to use the technology to work
with online teaching. The issue lies in how to match the technical knowhow and the pedagogy,
in order to use technology properly in the online environment. More exploration is needed for
ABC University to understand what interaction is needed and to match the interaction needs with
the right technology.
Implications for Assessment
By definition, assessment is the systematic collection, review, and use of information
about educational programs (Clark & Rust, 2006). Every stakeholder has valid reasons, from
their own perspective, to be in favor of certain ways to conduct assessment (Stark & Lattuca,
1996). Because there are many aspects involved, the focus of assessment should be on what is
the most important. The purpose of assessment is to improve student learning results (Palomba
& Banta, 1999). This opinion was reflected in Chinese respondents’ responses. How do faculty
know students are learning? This is probably the most important question. It was repeatedly
mentioned by the respondents. Sadly, as mentioned by many respondents, there is not a clear
answer.
In order to gauge how students are learning, the assessment at ABC University has to be
carefully designed and implemented. More importantly, after the implementation of an
assessment plan, the institution needs to continuously examine, share, and act on assessment

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findings. The ultimate value of assessment is to use the results to guide action (Shulman, 2007).
There need to be multiple ways to assess student learning, including a final exam. There should
be regular reexamination of the assessment processes to make assessment a routine activity of
teaching and learning (Palomba & Banta, 1999). There also needs to be participation and
involvement from people on and off campus, including students, parents, the public, faculty,
institution, and government representatives (Duderstadt, 2000).
One possible assessment tool for the ABC University to consider is a student learning
portfolio that students and teachers can use to document and review the learning progress. The
portfolio lists learning goals and records what the student has accomplished; the portfolio should
also clearly show how the student gets to where the student is heading. In the process of creating
the portfolio, the students will take the leading role. Periodically, the teacher and students will
work together to evaluate their learning progress in terms of knowledge, skills, attitudes, and
behavior changes (Shavelson, 2007). Adjustments will be made to the goals when needed.
Section Summary
This section discussed the implications for quality assurance in China. The suggestions
are based on the comments and responses from the respondents. These suggestions provide a
glimpse of what Chinese faculty perceived to be the action needed for quality assurance. These
suggestions also fill a gap within the Chinese literature, which lacks details of quality assurance.
5.6 Limitations
This study has several limitations that must be borne in mind. The first limitation is
about the selection of quality indicators included in the study. Ideally, the selected quality
indicators should holistically represent the quality standards from the international community.
In this study, the selected U.S. indicators, 31 of them, only covered a portion of them. There are

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standards from other countries and regions that can be useful, too. For example, the European
Union has its own quality standards for online education. It would have been very useful to
include those quality standards. To overcome this limitation, more indicators, from different
international resources, can be included in future studies.
The second limitation lies in the cross-cultural data collection process. Even though
efforts were made to ensure the clarity of the survey content, without a doubt the participants
could interpret the meaning of the questions differently, based on their knowledge of online
education; or they may have had different levels of ability to express themselves clearly and
accurately. The misunderstandings could also have happened when the responses were
interpreted, coded and translated from Chinese to English.
The third limitation lies in the scope of this study. The study was based on a single
institution. The sample included the online faculty and teaching assistants from one institution in
China. The study results can only represent ABC University’s online faculty’s opinions. But
China is a big country, and there are different types of institutions, with variations of institutional
focus, history of teaching, and reputation for quality. Future research could include more
institutions to repeat this study.
The fourth limitation lies in the participant population. The online faculty is the only
group that participated in the study. But there are other stakeholders whose opinions are also
important to the quality of online education. These stakeholders include staff, administrators,
students, family members, government agencies, and employers. To manage the scope of this
study, only the faculty group was included in the study. For future study, it will be valuable to
consider other stakeholders’ opinions.

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Lastly, the study only utilized the survey method to capture faculty perceptions. When
participants answered the questions in the survey, their answers were based on the moment they
reflected on their thinking and remembered experiences, which could be different than the actual
situation. There are other research methods, such as the interview, that can be used to answer
some of the why questions.
5.7 Future Study
This study explored Chinese faculty perception of the issue of quality standards. In
addition to the meaningful findings that could be useful to Chinese quality assurance for online
education, this study also serves as a conduit that leads to possibly future studies. One
possibility for future study is to select more institutions from different regions in China. For
example, one institution can be identified from the national research universities and one from
the regional colleges, with a combination of program size and discipline focus. Another
possibility is to replicate the study in other countries. By doing so, the researcher could further
test the hypothesis that quality standards can be transported cross-culturally. Future study could
also include quality standards for specific disciplines. In this study, the Chinese faculty
recognized the need to have discipline-specific standards. Similar studies can be used to study
quality standards for specific disciplines.
5.8 Conclusion
Online education has the potential to help address the higher education access problem.
It has proven its value as an important component of higher education. Despite its many benefits,
the quality of online education has worried stakeholders. It is commonly agreed that in order to
improve the quality of online education, it is necessary to develop quality standards that are
suitable to the educational environment.

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This study explored the extent to which the U.S. quality indicators for online education
are perceived by Chinese faculty. The findings from qualitative and quantitative results
confirmed that the U.S. quality standards are perceived as relevant by Chinese faculty. The
respondents rated highly the importance of quality indicators, and agreed that these benchmarks
reflect their criteria of quality. The confirmed relevance of the U.S. standards provides the
rationale for China to borrow and learn from these U.S. based quality standards. The results also
indicate that theoretically, borrowing and learning from international quality standards can be
beneficial to Chinese online education. The findings correspond with other Chinese scholars’
points of view and align with the goals set by the Chinese Ministry of Education. By adopting
quality standards from the international community, the process of developing Chinese quality
standards can be more efficient and more likely to succeed. Doing so also helps Chinese
institutions make better connections to the international higher education community.
The findings from this study provide a better understanding of the online education
quality issue in China. Much Chinese literature has mentioned concerns for the quality of online
education. However, there have not been many studies that have provided specific suggestions
to guide Chinese quality assurance practices. The results from this study include the specific
information about the quality issue in China, including resource allocation, faculty qualification,
online pedagogy, student motivation, and evaluation methods. With this understanding, quality
assurance issues in Chinese online education can be better addressed.
Could there be shared international standards? There has been a great deal of controversy
about this issue. Pro-common standard scholars have suggested that common educational
standards are necessary to ensure quality, and that the basic principles of teaching and learning
should apply regardless of the settings (Herrington, Jung, & Latchem, 2012). The results from

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this study reflect such an opinion. The strong sense of relevance perceived by the Chinese
faculty suggests that it is possible to have such common quality standards. The findings from
this study advance the argument for a set of common quality standards that can be applied across
borders. There is a set of standards that could conceivably be used across borders. There is
application for the standards developed by one country to be applied cross-culturally.
On the other hand, this study confirmed that educational quality is subjective. The
differences between educational settings make it difficult to have common standards. This
argument was reflected by Chinese respondents in this study. The respondents strongly argued
that there are differences that exist between educational settings, and quality standards should
reflect such differences. Special attention is needed when quality standards are developed and
applied across borders.
In addition, the findings from this study show that the U.S. quality standards for online
education are not perfect. The indicators focus on inputs, not on the outcomes that were
suggested by the Chinese faculty to measure, which include students are motivated to learn, and
faculty are motivated to improve teaching, etc. In order to do so, additional indicators need to be
identified and implemented. In order for these U.S.-based quality indicators to be adopted
successfully in China, these indicators need to be modified. The researcher proposed a
modification process to take into the consideration the uniqueness of local educational settings
by adding additional indicators that reflect the needs of local settings. The additional indicators
should include indicators to measure outcomes, and should consider including indicators that are
unique to local settings.
The study concludes that shared international quality standards for online education can
have two parts. One part is applicable to every educational setting and the indicators in this part

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are agreed upon by stakeholders from different countries. The other part is unique to each local
setting. Quality indicators in this part take into consideration the locality of the educational
environment and reflect different stakeholders’ opinions.

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APPENDICES

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Appendix 1
Expert Review of the Selected Quality Indicators
Purpose
You are invited to provide expert opinion on each of these 48 quality indicator’s legitimacy in
relation to online program quality. The definition of these 7 categories are:
You are asked to do three things:
1. Indicate whether you think each item should be included in this quality indicator pool.
2. If you think it should be included, please assign a category to this item.
3. Indicate whether you want to provide more comments on this item.
Forty-eight U.S. Online Education Quality Indicators
Institutional support category addresses institutional mission statement, infrastructure
development, resource allocation, and incentives for faculty to get involved in online education.
1.

Online learning is incorporated into the institution’s governance and academic oversight.

2.

The institution has defined the strategic value of online learning to its stakeholders.

3.

The institution provides sufficient resources to support online course offerings.

4.

There is institutional policy regarding the use of copy right materials.

5.

The institution has clear, specific, published policies related to academic integrity.

6.

Faculty are provided financial incentives to develop and teach online courses.

Technology and technology support category addresses policies and processes regarding
technology and technical support, technical standards, technical skills for teaching and learning,
and training.
7.

The institution has clear, specific, and published policies related to the use and
safeguarding of student information.

8.

Prerequisite technical skills are identified and clearly stated before faculty teach online
courses and students enroll in an online program.

9.

There is a documented technology plan that guides the technology investment choice and
implementation.

10.

Technical assistance and training are provided for faculty and students.

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11.

Learning outcomes, not the availability of existing technology, determine what
technology is used.

12.

The technology delivery systems are highly reliable. A centralized system provides
support for building and maintaining the online education infrastructure.

Course design and development category addresses policies and procedures that are
related to course structure, design, and content development processes.
13.

There are guidelines regarding online course design and course material development.

14.

Instructional materials, course syllabi, and learning outcomes are reviewed periodically to
ensure that they meet program requirements.

15.

The course is organized into units and lessons. Each unit has an overview that describes
the objectives, activities, and resources that frame the unit.

16.

Online courses are designed to require students to engage themselves in analysis,
synthesis, and evaluation as part of their courses and program requirements.

17.

Each lesson includes a lesson overview, content and activities, assignments, and
assessments to provide multiple learning opportunities.

18.

Expectations for student assignments, grade policy, and faculty responses are clearly
provided in the course syllabi.

19.

The online course design provides opportunities for appropriate instructor-student,
student-student, and student-content interaction.

20.

The course adequately addresses the needs of students with disabilities via alternative
instructional strategies and/or referral to special institutional resources.

Teaching and learning category addresses online student preparedness, course learning
objectives, faculty teaching practice, and teacher-student interactivity.
21.

Netiquette expectations regarding lesson activities and email communications are clearly
stated.

22.

During online teaching, faculty has the flexibility to provide students with supplemental
information and content.

23.

Information literacy and communication skills are incorporated and taught as an integral
part of the curriculum.

24.

There are instructions and suggestions on how to study and how to use the instructional
materials.

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25.

There is instruction that provides students with multiple learning paths to master the
content.

26.

Student-centered instruction is considered during the course development process.

27.

Students are instructed in the proper methods of effective research, including assessment
of the validity of resources.

28.

Feedback on student assignments and answers to student questions are constructive and
are provided in a timely manner.

29.

Students are encouraged to collaborate in a variety of ways, including web conferencing
and instant messaging.

30.

Student-to-student communication is promoted as part of lesson activities.
Communication such as threaded discussion forums and online chat are regularly used.

31.

Online course offerings are coherent and comparable in academic rigor to traditional
instructional formats.

Faculty support category address issues related to providing support to faculty members
when they develop and teach online courses, both technically and pedagogically.
32.

Faculty are assisted in the transition from classroom teaching to online teaching.

33.

Faculty receive training and materials related to fair use, plagiarism, and other relevant
legal and ethical concepts.

34.

Faculty are trained with respect to the best practices of online teaching and learning.

35.

There is an active peer-mentoring program for online faculty.

36.

Faculty are trained with respect to learner needs, instructional approaches, and the use of
educational technology.

Student support category helps online students to prepare themselves for online learning,
engage faculty, interact with other students, and use resources such as the library.
37.

Tutoring is available as a learning resource for students.

38.

Students have access to effective academic, personal, and career counseling services.

39.

There is a structured system to address student complaints.

40.

Prior to enrollment, students are advised of the commitment, self-directed learning, and
time needed to succeed in online learning.

41.

The institution provides orientation when students start the online program.

42.

The institution regularly evaluates the effectiveness of the student support services for
improvement purposes.

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Assessment category relates to policies and procedures that address how the institution
evaluates online learning, including assessment of courses, student learning.
43.

There are clear standards and expectations for faculty to engage in online teaching.

44.

Each course is evaluated at the end of each semester. The findings are used as a basis for
improvement.

45.

The teaching/learning process is assessed through evaluation processes that use several
methods and applies specific standards.

46.

The results of student evaluations are available to the instructor of the course.

47.

Ongoing assessments are conducted to verify each student's readiness for the next lesson.

48.

Student opinions are systematically sought as one basis for evaluating and improving
teaching purposes.

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Appendix 2
Reverse Translation Results (Chinese to English)
网络教学被纳入学校的学术和行政管理结构。
The web-based instruction was incorporated into the university's academic and administrative
management.
教师们明确了解网络教育的价值。
The teachers have clearly understood the value of web-based teaching.
学校为网络课程提供了足够的资源。
The school has provided sufficient resources for the web-based course.
学校制定了关于学术诚信的规定, 包括版权资料的使用， 学生身份的验证。
The school formulated regulations for academic integrity, including using copyrighted material,
and verifying the student's identity.
学校有一整套关于选择和实施网络教育技术的方案。
The school has designed a complete scheme about web-based technology selection and
implementation.
学校颁布了关于保护和使用学生个人信息的规定。
The school declared regulations to protect individual student's information.
学校明确规定了教职人员开设网络课程之前就应具备的技术和技能。
The school clearly formulated teacher's technical skills before they start web-based teaching.
学校协助教师从传统课堂授课过渡到网络授课。
The school helps teacher transition from traditional teaching methods to web-based teaching.
教师参加过关于如何合法使用素材、避免抄袭，以及其他有关道德法律的培训。
Teachers have joined training which taught them how to legally use information, avoid
plagiarism, and other moral training.
教师们受过优质网络教学的培训。
The teachers have been trained by the high quality web-based online courses.
网络教师之间有一个以老带新的机制。

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That experienced teachers train the new teacher is the rule in the web-based teaching.
教师们了解网络学习者的需求。
Teachers understand the needs of online learners.
学校有专门机构处理网络学生的投诉。
There is a special agency in school to deal with the student complains.
网络学生入学时，学校提供入学教育和指导。
The school will provide an orientation program as a new learner enters to the program.
网络学生能够得到有关如何有效学习、个人心理，以及职业规划方面的咨询服务。
The web-based student will have the information and service needed in order to know how to
study effectively, including individual psychological consulting and career planning services.
学校定期对学生支持服务机制进行评估，以期完善和提高服务质量。
网络课程的设计和开发有规可循。
There are rules to design and develop the online courses.
学校定期检查课程大纲，教材，和教学效果。
The school will regularly inspect syllabi, teaching materials, and teacher's performance.
作为课程的一部分，网络课程要求学生对其学习进行自我分析、总结和评估。
As a part of the curriculum, online courses require the student to self-analyze, summarize and
evaluate his/her learning.
网络课程的每个单元都有一个概述性描述来阐述学习目标和教学活动。
Each unit of the online course has a general situation to describe the learning objectives and
teaching activities.
网络课程教学大纲中明确规定了教师对学生作业，成绩， 和答疑的要求。
The online course clearly formulated the requirement to the teacher about students' homework,
grades and questions.
网络课程提供了师生之间、学生之间，以及学生和教学内容之间的互动。
The online course provides a good interactive platform for teacher and student, student and
student, and student and teaching materials.
课程大纲明确规定了网络课程的行为规范，包括教学活动、讨论和电子邮件交流等。

114

The school syllabi clearly formulated the online course's behavioral norm, teaching actives,
discussion, Email, and so on.
有关信息技术的基本常识和交流技巧成为网络课程教学的一个组成部分。
The basics of integration of Information Technology and the skill of interchange already have
become a part of the online course.
网络学生得到学习方法上的指导，包括评估资料的有效性和资料使用的版权性。
教师对学生的作业提供及时的有建设性的反馈。
The teacher will give positive feedback right away for the student's homework.
作为网络课程教学资源的一部分，教师为学生提供课后辅导。
As a part of the online course, the teacher will provide teacher's guidance after class for the
students.
在网络课程结束时，所有学生将对课程进行评估。
As the end of the online course, all students will evaluate the curriculum.学生对课程评估的结
果要提供给教师以便改进教学。
The results of the evaluation of the curriculum will be provided to the teacher in order to
improve his/her teaching.
教师进行实时评估以了解学生的学习状况。
The teacher evaluates in real time so they know the situation of students' study.
为了完善教学，教师系统性地搜集学生的反馈意见。
To improve and perfect teaching, the teacher will systematically gather feedback from students.

115

Appendix 3
Cognitive Interviewing Protocol
Cognitive interviewing explores how piloting survey participants understand, process,
and respond to what is presented in the questionnaire. The think-aloud method asks respondents
to verbalize all thoughts about survey questions. This gives the researcher an opportunity to
understand potential misunderstandings or difficulties in answering the questions. This process
focuses on these questions: Do respondents have problems with instructions or explanations? Is
the meaning of each question clear? Are some questions too sensitive, or do they produce biased
answers?
The outcomes of the cognitive interviews are to identify item-specific recommendations
for wording changes, clarification of questions, problems with item sequence, problems with
survey length, and limits on what a respondent can and will answer.

116

Appendix 4
English Version Survey
Study Title: Exploring Chinese Faculty’s Perception of Online Program Quality Indicators

Purpose of the study: In order to study the quality standard for online education, you are invited
to participate in this survey to provide faculty opinions on what matters to the quality of online
education. The survey has 15 questions that may take 25-30 minutes to complete.
Participation: Participation in this study is entirely voluntary. Your opinion in this study will help
us to better understand the perceived quality indicators by Chinese faculty.
What will be done: You will complete an online survey, which will take approximately 25-30
minutes to complete. Data analysis will follow standard quantitative analysis.
Risks or discomforts: There are no anticipated risks or discomforts associated with participation
in this study. If you feel uncomfortable answering any question, you may skip that question or
quit the survey at any time by leaving the survey website.
Confidentiality: Your Confidentiality will be protected to the maximum extent allowable by
law. Online surveys will be completed anonymously and no identifying information will be
collected. And the questions do not ask for any sensitive information. Your answers to the
survey questions will have no connection to your identity.
How the findings will be used: the findings will be used to find important aspects in order to
have good quality online education program. Recommendations for ensuring online education
quality will be proposed.
Contact information: If you have questions or concerns regarding your rights as a participant, or
are dissatisfied with any aspect of this study, you may contact Dave Dai (daix@msu.edu ), John
Dirkx (dirkx@msu.edu) or MSU IRB irb@msu.edu.
Consent: By beginning this survey, you acknowledge that you have read this information and
agree to participate in this study, with the knowledge that you are free to withdraw your
participation at any time without consequence.
Please select:
Yes, I want to take the survey
No, exit the survey

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Q1. What are the top three objectives for online education programs?
Q2. What are the top three characteristics of high quality online programs?
Q3. Do you have any concern for the quality of current online education? Why?
Q4. Do you have any suggestions to improve the quality of online education?
Q5. Please rank the following factors by their importance to online education quality:
Technology selection, implementation, and support
Course design and development
Evaluation methods and processes
Support to faculty
Support to students
Online teaching and learning strategies
Institutional commitment and support
Q6. Comparing with your face-to-face teaching experience, are you satisfied with your online
teaching experience? Why?
Q7. If you have a choice, will you teach an online course in the future? Why?
Q8. How often do you use following methods to assess student learning?
Quiz
Essay
Exam
Project
Presentation
Discussion forum
Email students
Lab work
Talk to students via phone
Send text message
Virtual office time
Q9. Based on your perception, please rate the following quality indicator statements on: 1) The
importance of these indicators, where: 1 = Not important, 2 = Slightly important, 3 = Moderately
important, 4 = Important, and 5 = Not sure. 2) The presence of these indicators at your
institution, where: 1 = Not present, 2 = Partly present, 3 = Present, and 4 = Not Sure.

118

Online learning is incorporated into the institution’s governance and academic structure.
Faculty clearly understand the value of online education.
The institution provides sufficient resources to support online course offerings.
The institution has clear, specific, published policies related to academic integrity.
There is a documented technology plan that guides the technology investment choice and
implementation.
6. The institution has clear, specific, and published policies about using and safeguarding of
student information.
7. Prerequisite technical skills are identified and clearly stated before faculty teach online
courses.
8. Faculty are assisted in the transition from classroom (face-to-face) teaching to online
teaching.
9. Faculty receive training and materials related to fair use, plagiarism, and other relevant
legal and ethical concepts.
10. Faculty are trained with respect to the best practices of online teaching and learning.
11. There is an active peer-mentoring program for online faculty.
12. Faculty are trained with respect to online learner needs.
13. There is an institutional structured system to address student complaints.
14. The institution provides orientation when students start the online program.
15. Students have access to effective academic, personal, and career counseling services.
16. The institution regularly evaluates the effectiveness of the student support services for
improvement purposes.
17. All students are encouraged to evaluate the course at the end of the class.
18. The results of student evaluations are available to the instructor of the course for
improvement purposes.
19. Ongoing assessments are conducted to verify each student's readiness for the next lesson.
20. Student opinions are systematically sought as one basis for evaluating and improving
teaching purposes.
21. Guidelines are used to design and develop online courses.
22. Instructional materials, course syllabus, and outcomes are reviewed periodically.
23. Online courses are designed to require students to engage themselves in analysis,
synthesis, and evaluation as part of their courses and program requirements.
24. Each learning segment has an overview that describes the objectives and activities.
25. Expectations for student assignments, grade policy, and faculty responses are clearly
provided in the online course syllabus.
26. Online course design provides opportunities for appropriate instructor-student, studentstudent, and student-content interaction.
27. Netiquette expectations regarding lesson activities, discussions, and email
communications are clearly stated in the course syllabus.
28. Information literacy and communication skills are taught as an integral part of the online
curriculum.
29. Students are instructed in the proper methods of research, including assessment of
resource validity and use of copyright material.
30. Feedback on student assignments is provided in a timely constructive manner.
31. Tutoring is available as a learning resource available to students.
1.
2.
3.
4.
5.

119

Q10. The questions you just completed are based on benchmarks from U.S. online education
framework. Do these benchmarks reflect your criteria of quality for online education? Why?
Q11. Should these benchmarks be adopted by Chinese online education programs? Why?
Q12. Please provide proper response that best describes you.
What is your gender?
What is your age?
How many years have you taught at higher education institution(s)?
What is your current academic rank?
How many years have you taught online course(s)?
Do you also teach the face-to-face version of the same course?
Do you teach an introductory course or a major required course?
On average, how many students do you have in your online class?
How many different online courses have you taught so far?
How many times you have taught your current online course?
What is your academic discipline?

120

Appendix 5
Chinese Version Survey
研究题目： 《探索中国高校教师对网络教育质量标准的看法》
研究目的：为了研究网络教育的质量标准，我们诚挚邀请您参与此项问卷调来了解中国
教师如何看待网络教育质量标准。调查包括 15 个问题，可能需要 25-30 分钟完成。
参与方式：参与这项研究是完全自愿的。您的真实和完整的回答将有助于我们更好地理解
中国教师如何看待网络教育质量标准。
问卷调查过程：您可能需要 25-30 分钟完成这项问卷。数据分析将遵循标准的定量分析。
风险或不适：这项在线问卷调查没有任何预期的风险。如果你觉得不愿回答一个问题，你
可以跳过这个问题，或者在任何时候退出问卷调查。
保密性：您的隐私将按法律规定得到最大程度的保护。这项在线问卷调查将以匿名方式完
成。在问卷调查数据的收集和分析的过程中，您的隐私会得到保护。问卷也不会问任何敏
感信息。
研究成果：调查结果将被用来帮助于更好地理解中国教师如何看网络教育质量标准。研究
报告会包括确保网络教育质量的建议。
联系方式：如果您有问题或疑虑，或者对本研究有任何不满意，您可以联系戴晓
(daix@msu.edu),John Dirkx (dirkx@msu.edu) 或 MSU IRB 办公室 irb@msu.edu。
同意参与：阅读此信息后，如果您同意参与这项问卷调查，请开始问卷调查。您可以在任
何时候退出问卷调查。
戴晓 先生, John Dirkx 博士
美国密歇根州立大学 高等教育管理系
请选择：
同意参与问卷调查
不同意参与问卷调查

121

问题 1. 您认为网络教育办学的宗旨是什么?
问题 2. 您认为高质量的网络教育办学有哪三个特征?
问题 3. 您对目前网络教育办学质量有无担忧? 为什么?
问题 4.您对提高网络教育办学质量有何建议?
问题 5.根剧对网络教育办学质量的重要性, 请对下列各方面排序:
技术的选择，实施和支持
课程的设计与开发
教学效果的评估
学校对教师的支持
学校为学生提供的支持和服务
教学策略和技巧
学校对网络学习环境的重视度
问题 6.与传统课堂教学相比, 您对网络课程的教学效果满不满意? 为什么?
问题 7.如果您可以自由选择，您是否愿意继续教授网络课程？为什么?
问题 8.在您教授的网络课中, 您多久使用以下方法？
小测验
命题作文
考试
与课程相关的小题目
课堂展示、陈述
讨论
实验室（包括虚拟实验室）
电话与学生交流
电子邮件
发短信给学生
虚拟办公室
问题 9.请评估以下质量指标： 1）评估这些指标的重要性，其中, 1 = 不重要，2 = 一般重
要，3 = 比较重要，4 = 重要，5 = 不确定。2）评估这些指标在你的大学的存在。其中, 1
= 不存在，2 = 部分存在，3 = 存在，4 = 不确定：
1. 网络教学被纳入学校的学术和行政管理机构。
2. 教师们明确了解网络教育的价值。
3. 学校为网络课程的开设提供了足够的资源。

122

4. 学校制定了关于学术诚信的规定, 包括版权资料的使用和网络学生身份的验证等。
5. 学校有一整套关于选择和实施网络教育技术的方案。
6. 学校颁布了关于保护和使用学生个人信息的规定。
7. 学校明确规定了教职人员开设网络课程之前就应具备的技术和技能。
8. 学校协助教师从传统课堂授课过渡到网络授课。
9. 教师参加过关于如何合法使用素材、避免抄袭，以及其他有关道德、法律的培训。
10. 教师们受过优质网络教学策略的培训。
11. 网络教师之间有一个以老带新的机制。
12. 教师们了解网络学习者的需求。
13. 学校有专门机构处理网络学生的投诉。
14. 网络学生入学时，学校提供入学教育和指导。
15. 网络学生能够得到有关如何有效学习、学习心理，以及职业规划方面的咨询服务。
16. 学校定期对网络学生服务机构进行评估，以期完善和提高服务质量。
17. 在网络课程结束时，所有学生对课程进行评价。
18. 学生对课程评价的结果要反馈给教师以便改进教学。
19. 教师在教学中采用实时评估来了解学生的学习状况。
20. 为了完善教学，教师系统性地搜集学生的反馈意见。
21. 网络课程的设计和开发有规可循。
22. 学校定期检查课程大纲、教材、和教学效果。
23. 作为课程的一部分，网络课程要求学生对其学习进行自我分析、总结和评价。
24. 网络课程的每个单元都有一个概述性描述来阐述学习目标和教学活动。
25. 课程教学大纲中明确规定了教师对学生作业，成绩， 和答疑的要求。
26. 网络课程提供了师生之间、学生之间，以及学生和教学内容之间的互动。
27. 课程大纲明确规定了网络课程的行为规范，包括教学活动和网上讨论等。
28. 有关信息技术的基本常识和网上交流技巧成为网络课程教学的一个组成部分。
29. 网络学生得到学习方法上的指导，包括资料的可靠性和资料使用的合法性。
30. 教师对学生的作业提供及时的和有建设性的反馈。
31. 作为网络教学的一部分，辅导教师为学生提供课后辅导。
问题 10: 考虑到上述指标是基于美国教育认证机构制定的网络教育质量标准，这些指标是
否反映了您对网络教育质量标准的看法? 为什么?
问题 11: 您认为中国高校的网络教育质量标准应不应该包括这些指标？为什么?
问题 12: 请选择最符合您情况的选项。
性别?
您的年龄？
高校任教年数？
学术职务？
您教了多少年的网络课程？
您也教相同的面对面课程吗？

123

您教的是公共课还是专业必修课？
您的网络课平均有多少学生？
您迄今为止上过多少门不同的网络课？
目前所上的这门网络课，您上过多少次了？
您的学科领域？

124

Appendix 6
Seven Factors Categorization Based on U.S. Literature
Figure 15. Seven Factors CFA Results

125

Table 17. CFA Results for Seven-Factors

126

Appendix 7
Five-Factor Categorization for Quality Indicators of Online Education
Figure 16. Five Factors CFA Results

127

Table 18. Five Factors CFA Results

128

Institutional Mission and Support
1. Faculty clearly understand the value of online education.
2. The institution provides sufficient resources to support online course offerings.
3. The institution has clear, specific, and published policies related to academic integrity.
4. The institution has clear, specific, and published policies related to the use and
safeguarding of student information.
Faculty Support
5. Faculty are assisted in the transition from classroom (face-to-face) teaching to online
teaching
6. Faculty receive training and materials related to fair use, plagiarism, and other relevant
legal and ethical concepts.
7. Faculty are trained with respect to the best practices of online teaching and learning.
8. There is an active peer-mentoring program for online faculty.
Student Support
9. There is an institutional structured system to address student complaints.
10. The institution provides orientation when students start the online program.
11. Students have access to effective academic, personal, and career counseling services.
Teaching and Learning
12. Instructional materials, course syllabi, and learning outcomes are reviewed
periodically.
13. Online courses are designed to require students to engage themselves in analysis,
synthesis, and evaluation as part of their courses and program requirements.
14. Each learning segment has an overview that describes the objectives and activities.
15. Feedback on student assignments and answers to student questions are constructive
and provided in a timely manner.
Assessment
16. All students are encouraged to evaluate the course at the end of the class.

129

17. The results of student evaluations are available to the instructor of the course for
improvement purposes.
18. Ongoing assessments are conducted to verify each student's readiness for the next
segment.
19. Student opinions are systematically sought as one basis for evaluating and improving
teaching purposes.

130

Appendix 8
Open-ended Questions Analysis Results
1. What are your beliefs about the objectives of online education?
2. What are the characteristics of online education programs with quality?
3. Are you satisfied with your online teaching experience? Why?
4. Is there any concern for the quality of current online education? Why?
5. If you have a choice, will you teach an online course in the future?
6. What do you suggest to improve the quality of online education in China?
7. Do these benchmarks reflect your criteria of quality for online education?
8. Should China adopt these benchmarks?

131

1. What are your beliefs about the objectives of online education?
Table 19. The Respondents’ Beliefs about Online Education

Inductive Themes
Construct lifelong learning
environment

Expand delivery methods

Develop student skills

Efficiently utilize resources

Educational equality

Dissimilate knowledge

Participant
Responses

N

108

97

67

64

36

36

24%

To build a lifelong learning
environment.

22%

Enable anywhere, anytime teaching and
learning.

15%

Help students develop skills and be better
prepared for work.

14%

To share and maximize educational
resources.

8%

Provide educational opportunity and ensure
equality.

8%

To widen channels for knowledge
dissimilation.
Understand and explore proper pedagogy
for online education.

Pedagogy

20

4%

Other

14

3%

Improve quality

8

2%

# of responses

To improve quality of education.

450

132

2. What are the characteristics of online education programs with quality?
Table 20. The Characteristics of Quality Online Programs
Inductive Themes

N

Participant Responses

155

Provide open access and flexible learning
28% opportunities.

Resources

99

Have adequate resources and utilize resources
18% efficiently.

Quality faculty

63

12% Have responsible and competent faculty.

Access

Management

42

8%

Have systematic process to evaluate the teaching and
learning.

Educational
equality

38

7%

Open educational opportunities to variety of students.

Interaction

32

6%

There is interaction between teacher and students.

Student motivation

32

6%

Students have the motivation to participate in online
learning.

Clear purpose

32

6%

There are clear purposes defined for online learning.

5%

Have an advance technology platform that faculty know
how to use.
Have a supportive environment for students, and
provide timely feedback.

Technology

29

Student support

15

3%

Other

8

1%

# of answers

545

133

3. Are you satisfied with your online teaching experience? Why?
Table 21. Faculty’s Reason for Satisfaction
Inductive Themes
Teaching flexibility and selfsatisfaction

N

Example responses

63

43%

Teachers can teach anywhere, anytime.
There is self-satisfaction.

Educate more students

49

34%

Online learning meets a variety of needs of
learners.

Information access

24

17%

There is plenty of information available
online.

Other or unclear

9

6%

# of responses

145

Table 22. Faculty’s Reason for Dissatisfaction
Inductive Themes

N

Example Responses

Lack of interaction

50

21%

There is not enough interaction between
teacher and students.

Students’ motivation

45

19%

Students are not self-motivated. Some
students’ goal is to get a diploma.

21%

The course evaluation structure and processes
are not well developed.

Evaluation and feedback

48

Learning results

41

18%

It is difficult to know and monitor student
learning results.

Online teaching pedagogy

35

15%

Teachers need to know and use proper
pedagogy for online teaching.

Other or unclear

14

6%

# of responses

233

134

4. Is there any concern about the quality of online education? Why?
Table 23. The Respondents’ Reasons for Having Concerns
Inductive Themes

N

Example Responses

Student motivation

86

25% Some students did not put effort into learning.

Faculty quality

53

16% Online teacher motivation. Quality needs to improve.

Evaluation

40

12% It is difficult to know learning results.

Content

37

11% Course materials are not updated regularly.

Management

29

9% There is not enough monitoring and managing process.

Interaction

31

There should be more timely communication and
9% interaction between students and teachers.

Quality focus

26

8% Suspicion exists about the quality of online education.

Policy issue

14

4% The online environment needs to be regulated.

Acceptance

15

4% Online education has not being widely accepted.

Other

8

2%

# of responses 339

135

5. If you have a choice, will you teach an online course in the future?
Figure 17. The Respondents’ Willingness to Teach Online Courses

Table 24. Reasons for Not Willing to Teach Online Courses
Inductive Themes

N

Evaluation process

22

Online teaching is not included in the teacher
36% evaluation structure.

18

The results of online teaching are not as good as
30% face-to-face teaching.

10

Online teaching objectives are not being
16% emphasized.

8

Students are not willing to put in the effort to
13% learn.

The results of online
teaching

Online teaching objectives

Student motivation

Example Responses

The focus of online learning

2

3%

Other

1

2%

# of responses

The management of online teaching focuses on
monetary incentives.

61

136

Table 25. Reasons for Willing to Teach Online Courses
Inductive Themes for
Willing to Teach

Self-satisfaction; flexibility
Online education is the
future

N

Example Responses

Self-satisfaction, improve teaching efficiency,
143 43% teach with flexibility.

44

Online education is the future and I want to get
13% involved.

Alternative

41

Online education is a good alternative to face12% to-face teaching.

Results

39

12% Online education has good results.

Professional development

31

9%

Teaching online also helps me in terms of
expanding professional knowledge and
developing online course designing skills.

Provide for students

17

5%

Provide student flexible ways for education.
The institution provides incentives to teach
online courses.

Incentives

14

4%

Share resources

5

1%

# of responses 334

137

6. What do you suggest to improve the quality of online education in China?
Table 26. Suggestions to Improve Quality in China
Inductive Themes

Example Responses

Faculty
qualification

Improve faculty qualification; provide training and
22% professional development.

Evaluation
processes

Reform evaluation process, including multiple ways to gauge
20% student learning; not only rely on final exam.

Management

15% Build management structure and reinforce management.

Pedagogy

Various suggestions for pedagogy and course material
12% development.

Resources

There should be more resources available to support online
11% learning.

Orientation

Improve orientation process; strengthen the requirement for
11% graduation.

Policy and
acceptance

Need more support from government; more acceptance from
10% society.

Learning platform

9%

Provide open platform, ensure bandwidth, and improve video
conferencing ability.

Interaction

9%

Encourage better communication and interaction.

Other

2%

138

7. Do these benchmarks reflect your criteria of quality for online education?
Figure 18. The Reflection of Quality Criteria

Table 27. Reasons for the Reflection of Quality Criteria
Inductive Themes

N

Generality versus
specifics

83

25% This set is not discipline specific.

60

This set of benchmarks has a wide coverage of online
18% education quality.

56

China is at the beginning stage of online education.
17% China needs to learn from more matured practices.

Difference settings

50

The U.S. is different from China. The educational
standard for online education should consider such
15% differences.

Practical use

40

12% This set of benchmarks is useful and practical.

Commonality

27

8%

Other

19

6%

Coverage
China needs to learn

Example Responses

Quality of online education has common principles.
It does matter which country it is in.

# of responses 335

139

8. Should China adopt these benchmarks?
Figure 19. China’s Adoption of the U.S. Quality Indicators

Table 28. China’s Adoption of the U.S. Quality Indicators
Inductive Themes

N

Example responses

China needs standards

119

34%

China cannot reply on other countries'
standards; it should develop its own.

China can learn from the
U.S.

66

19%

China can learn from US in quality standards.

Useful or partially useful

65

19%

This set is very useful or partially useful.

Country difference

29

8%

Quality standards
commonality

19

6%

There are differences between the U.S. and
China.
China should adopt these U.S. benchmarks.

Other
Internationalization

20
17

6%
5%

Benchmarks need more
work

10

3%

# of responses

China needs to connect with international
standards.
This set of benchmarks needs more work.

345

140

Appendix 9
Factor Analysis to Further Reduce Factors
Table 29. Further Factor Reduction Results
Factor
1

2

Institutional support

.328

.684

Faculty support

.285

.665

Student support

.600

.535

Teaching and learning

.669

.375

Assessment

.789

.291

Figure 20. Factor Analysis Scree Plot

141

Appendix 10
Addendum
After the data analysis, the researcher went to the ABC University in the summer of 2014
to present the initial findings to a group of administrators and researchers from the Online
College. The feedback from the group was welcoming and positive. The findings were
perceived as important information for the Online College to continue the practices of quality
assurance.
The administrators requested a summary of the findings and the draft report of the study.
One important comment from the group was the cultural difference that needs careful
consideration. The point is that the understanding and interpretation of the key terminology of
online teaching and learning can be different between Chinese faculty and what U.S. scholars
perceive. Even though the terminology translation is the same, the meaning or the degree of the
practice can vary. The researcher provided two points view as responses. One is that this study
did make the effort to avoid misunderstandings and misinterpretations of the quality
indicators. That is why three Chinese visiting scholars participated in the pilot test, through
which the proper terms were verified to ensure the clarity to the survey participants. The second
point is that this comment points to additional research possibilities that can be more geared
toward cultural differences between China and the U.S. Specifically, a study can be conducted
to see whether there are significant differences between Chinese faculty and U.S. faculty when
they are presented with the same terminology from online education.
The researcher also learned that there are three suggestions made in the implications
section that were confirmed to be valid. The Online College has realized similar issues and has
started several initiatives over the past year. The first is about how to assess student

142

learning. The Online College has implemented an assessment structure that uses multiple ways
to assess student learning. This structure takes attendance, in-class performance, and exam
results to form a 20%+20%+60% structure. This structure shifts weight away from the final
exam results and provides a base for a continuing evaluation process on a weekly basis.
The second initiative is to include online teaching as part of faculty's workload and
promotion evaluation. This initiative is at the beginning stage, due to the organization changes
that require more time and debate. But the institution has realized the importance of including
online teaching as part of evaluating faculty professional performance. As discussed in Chapter
Five, the inclusion of online teaching will be a major motivation for faulty to participate in
online teaching and to improve their teaching performance.
The ABC University has also started another initiative, to offer online classes to oncampus students. All online classes offered to on-campus students were filled. The initial
results have been positive. The Online College is planning to offer more programs and courses
to on-campus students. This initiative helps address the issue of the low academic status for
online education in China. Online education in China has not been widely accepted partly
because of its perceived inferior quality in comparison to on-campus courses. By offering the
online courses to on-campus students, online classes can eventually improve their perceived
academic status. On- campus students taking online classes have been a regular practice in the
U.S., but new in China.

143

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