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Building student trust in online learning environmentsYe D.docx
1. Building student trust in online learning environments
Ye Diana Wang*
Department of Applied Information Technology, George Mason
University, Fairfax, VA, USA
(Received 30 December 2013; final version received 19 June
2014)
As online learning continues to gain widespread attention and
thrive as a
legitimate alternative to classroom instruction, educational
institutions and online
instructors face the challenge of building and sustaining student
trust in online
learning environments. The present study represents an attempt
to address the
challenge by identifying the social and technical factors that can
likely induce or
influence students’ perception about the trustworthiness of an
online course and
integrating the factors into a socio-technical framework that can
be empirically
validated. The methodology used and the data obtained from a
university-wide
survey conducted in an American university are reported in this
article. Feedback
from students with disabilities was further investigated, and the
result has
important implications for our understanding of disabled
students’ acceptance for
2. online learning.
Keywords: trust; student perception; e-learning; distance
education; disability;
accessibility
Introduction and background
The need for building student trust in online learning
environments
The tremendous advancements in information and
telecommunication technologies
and their adoption in education have opened up new avenues for
educational institu-
tions and students alike. As soon as the Internet became readily
available to the pub-
lic, distance education was transformed from old-fashioned
correspondence schools
using printed materials, telephone, TV, or videotape to today’s
Internet-based
e-learning or online learning (Bernard et al., 2009; Sims, 2008).
Although online learning continues to grow in both presence
and importance, its
potential is far from being fully utilized. There are still doubts
about the effective-
ness of online learning environments (see Hashem, 2011). In
addition, a higher num-
ber of e-learner dropouts, when compared with face-to-face
courses, have been
reported (Bell & Federman, 2013; Patterson & McFadden, 2009;
Tyler-Smith,
2006). This is especially true in the United States, where strong
and consistent
national quality assurance measures are lacking for online
4. of 1990 and Sections 504 and 508 of the Rehabilitation Act of
1973 require educa-
tional institutions to provide reasonable accommodations to
disabled students and
make their electronic and information technology, including
online learning applica-
tions, accessible. In order to receive accommodations, however,
disabled students
are required to disclose their disability to the instructor and
formally request the nec-
essary services or accommodations. Consequently,
unwillingness to disclose sensi-
tive information is a significant obstacle to their adoption of
online learning
(Bowker & Tuffin, 2002).
As retention and self-disclosure are increasingly becoming
challenges faced by
today’s educational institutions and online instructors, as well
as preventing the
widespread adoption of online learning, it is necessary to seek
answers from the
topic of trust, which is the “firm belief in the competence of an
entity to act depend-
ably, securely and reliably within a specific context”
(Grandison & Sloman, 2000,
p. 4). Research shows that trust is vital for ensuring effective
commitments and
reducing the level of uncertainty (Kramer, 1999; Luhmann,
2000). It has been
repeatedly identified as an important parameter in online
learning (Anwar & Greer,
2012; Pelet & Papadopoulou, 2012; Xu & Korba, 2005) for
several reasons: firstly,
in the case of online learning, trust is involved in the decision-
making process
5. prospective students employ when enrolling in online courses. It
is also a key factor
in preventing current students from dropping out. As pointed
out by O’Brien and
Renner (2002), establishing trust is essential for online student
retention. If prospec-
tive students are trusting, they are more likely to enroll in
online courses, thereby
easing enrolment problems; if current students are trusting, they
are less likely to
drop out, thereby easing retention problems (Ghosh, Whipple, &
Bryan, 2001). As a
result, trust in the online learning environment is a large
component in easing enrol-
ment and retention problems. Secondly, disclosure of personal
information depends
on trust (Briggs, Simpson, & Angeli, 2004). Since trust reduces
the perceived risks
involved in revealing private information, it is a precondition
for self-disclosure
(Anwar & Greer, 2012; Steel, 1991). In other words, if disabled
students are trust-
ing, they are more likely to disclose their disability to the
instructor and receive
accommodations, thereby easing acceptance problems. Thirdly,
although there are
legal requirements for equal access to online education in many
countries, there is
no financial provision for this so the cost and problems of
accommodating disabled
students’ needs must be borne by the providers. The students
therefore need to be
able to entrust the providing institutions to use all the best
means of provision at
their disposal. Finally, as a student’s trust in a teacher
determines the degree to
6. which that student will be open to being taught by that teacher;
trust is also a requi-
site component of a student–teacher relationship for maximal
learning to occur
(Wooten & McCroskey, 1996). Therefore, building and
maintaining students’ trust
in online learning environments is crucial to the success and
future of online
learning.
346 Y. D. Wang
The present study
The present study aims to address the challenge by identifying
the social and techni-
cal factors that can likely induce or influence students’
perception about the trust-
worthiness of an online course and integrating the factors into a
socio-technical
framework of trust-inducing factors. The proposed framework is
then validated
through a survey conducted within an American university. In
this study, student
trust in an online course is defined as “the degree to which a
student is willing to
rely on the e-learning system and has faith and confidence in
the instructor or the
educational institution to take appropriate steps that help the
student achieve his or
her learning objectives.” This definition is consistent with the
concept of trust found
in the education literature (Bulach, 1993; Ghosh et al., 2001).
7. The rest of the article begins with an introduction to related
work on trust in the
online learning context, describes the proposed socio-technical
framework, the
research methodology, and the results of the survey, and,
finally, ends with conclu-
sions and directions for future explorations.
Related work on trust in online learning
Due to the fact that trust has existed as long as the history of
human beings and the
existence of human social interactions, trust, with all of its
permutations, has been
studied in numerous disciplinary fields, such as philosophy,
psychology, manage-
ment, and marketing (Wang & Emurian, 2005). Since the
inception of the World
Wide Web, a great deal of research focus has been on trust in
electronic settings, for
example, e-commerce (Cheung & Lee, 2006; Ha & Stoel, 2009;
Jarvenpaa,
Tractinsky, & Vitale, 2000) and virtual communities and online
collaboration
(Al-Ani et al., 2013; Ridings, Gefen, & Arinze, 2002; Ziegler &
Lausen, 2004).
Compared to trust research in e-commerce and other fields,
there is a dearth of
literature on investigating trust in the context of online learning
(Hashem, 2011).
The earliest attempt was Xu and Korba (2002), who proposed a
trust model, based
on policy negotiation and public key cryptography, for solving
security and privacy
concerns inherent in distributed interactive e-learning
8. applications. The paper
pointed out the importance of maintaining a trustworthy e-
learning environment as
well as the need for trust evaluation mechanisms. Eight years
later, Liu and Wu
(2010) presented a survey, which aimed to give a “panoramic
view” (p. 118) on
trust and trustworthy online learning studies. However, the
review covered only a
limited number of studies at the intersection of trust and online
learning. The authors
further encouraged more proper trust models designed for
constructing reliable
online learning systems.
One line of research into trust and online learning is concerned
with security and
privacy issues of the online learning processes, platforms, or
environments. The
three most common approaches to trust establishment and
evaluation found in the
literature are as follows: (1) policy-based approaches are widely
used in security and
access control to change the behavior of large distributed
systems. Trust-related poli-
cies usually consist of authorization policies, which define the
authorized and unau-
thorized actions of a subject over an object (e.g., authorizing
individual access), and
obligation policies, which specify the positive and negative
obligations of a subject
toward an object (e.g., acquiring the user’s consent for
collecting data) (El-Khatib,
Korba, Xu, & Yee, 2003); (2) certificate-based approaches rely
on the use of digital
9. Distance Education 347
certificates and signatures (e.g., X.509, PGP [Pretty Good
Privacy]). A certification
authority, which represents a trusted party, issues a digital
certificate to identify
whether or not a public key truly belongs to the claimed owner
or to certify the
party is authenticated to be trustworthy or not (El-Khatib et al.,
2003); and (3) repu-
tation-based approaches are based on one’s own past experience
or the recommenda-
tions from third parties. In this type of approach, reputation is
used to measure trust
for online learning. Many examples of formal methods for
reputation assessment of
a site or of a user can be found on the Web, such as eBay and
Epinions (Anwar &
Greer, 2012).
In most cases a combination of the aforementioned approaches
to trust establish-
ment and evaluation is used. For instance, El-Khatib et al.
(2003) and Xu and Korba
(2005) focused on both policy-based and certificate-based
approaches when examin-
ing privacy and security standards and issues associated with
online learning. Anwar
and Greer (2012) presented a new model for facilitating trust in
online learning
activities by integrating reputation (reputation is calculated on
three dimensions)
with policies (guarantor vouches for credentials based on
reputation) in determining
10. trust.
A notable number of studies focus on using trust mechanisms to
collect suitable
and trustworthy learning resources in online learning
environments. Yang, Chen,
Kinshuk, and Chen (2007) identified the difficulties in finding
quality learning con-
tent and trustworthy learning collaborators to be the major
barriers to efficient and
effective knowledge sharing in virtual learning communities. To
overcome the afore-
mentioned barriers, the authors applied peer-to-peer (P2P)-
based social networks
with trust-management mechanisms, which classified peers
based on their content’s
quality. In order to tackle the challenges faced by online
learning providers in
presenting the most suitable learning resources to learners,
Carchiolo, Correnti,
Longheu, Malgeri, and Mangioni (2008) and Carchiolo,
Longheu, and Malgeri
(2010) exploited the idea of trustworthiness associated with
both learning objects
and peers in a P2P e-learning scenario. They proposed a trust-
and recommendation-
aware framework for searching personalized and useful learning
paths suggested by
reliable or trusted peers. Similarly, Liu, Chen, and Sun (2011)
presented a service-
oriented architecture-based e-learning model with quality
certification and trust
evaluation based on trust computing formulas, which aimed to
recommend high-
quality and trustable education services to learners.
11. Another line of research into trust and online learning is
concerned with people’s
trust and perception toward online learning. Jairak,
Praneetpolgrang, and
Mekhabunchakij (2009) investigated university instructors’ and
students’ perception
toward blended learning and fully online learning methods in
Thailand, using trust
as a predictor for online learning adoption in both delivery
methods. Hashem (2011)
examined Middle Eastern students’ attitudes toward online
education and the role of
new information technology in online education, based on
various factors that may
affect their trust. Both the aforementioned studies used a survey
methodology to col-
lect feedback from study participants. In addition, Pelet and
Papadopoulou (2012)
took a unique approach by investigating the effect of color on
memorization and
trust in online learning. The authors concluded that a careful
selection of colors,
which constitute an important variable for the design of online
learning systems, can
enhance students’ trust in the online learning environments and
the available con-
tent. This result reinforced the importance of interface design
factors in inducing
trust in online environments.
348 Y. D. Wang
As shown in reviewed work on trust in the online learning
context, none of the
12. work has thoroughly investigated the antecedents or
determinants of trust in online
learning environments. Therefore, a pressing need exists for a
deeper and more com-
prehensive understanding on the factors that influence student
trust in online learn-
ing. Without such understanding, it is difficult to build and
manage student trust in
an online environment, thereby making it more challenging for
educational institu-
tions and instructors to provide trustworthy, sustaining, and
successful online
courses.
Proposed socio-technical framework of trust-inducing factors1
A socio-technical framework of trust-inducing factors is
proposed in an effort to
synthesize existing literature on enhancing student or consumer
trust in virtual envi-
ronments. This framework is “socio-technical” because it
includes both social and
technical features of an online course (including its instructor
and the system on
which the course is built) that can likely induce or influence
students’ perception
about the trustworthiness of the online learning environment.
Compared to other
trust models or evaluation mechanisms proposed in the related
studies that have
been reviewed, the proposed framework is comprehensive since
it takes into account
not only different types of trust (i.e., policy-based, certificate-
based, and reputation-
based) but also the communicative styles of the instructor and
the interface design
13. of the online learning system. On the other hand, the framework
is not exhaustive in
the sense that it does not attempt to capture every possible
trust-inducing factor that
can be applied in an online course. Rather, it focuses on
articulating the most promi-
nent set of trust-inducing factors derived from numerous
previous studies and pre-
senting them as an integrated entity that can be evaluated
empirically.
The framework classifies 12 trust-inducing factors into four
broad dimensions:
namely, (1) credibility, (2) design, (3) instructor socio-
communicative style, and (4)
privacy and security. The dimensions are identified on the basis
of a semantic and
functional grouping of factors obtained from the literature.
Table 1 illustrates the
framework in detail, including the dimensions, trust-inducing
factors, and literature
sources for each dimension.
Specifically, the credibility dimension refers to the cognition-
based features, such
as previous experience or reputation of the online learning
system and the instructor,
which are usually formed prior to the current course; the design
dimension defines
the overall design quality and accessibility of the informational
and graphical com-
ponents of the online learning system; the instructor socio-
communicative style
dimension refers to the patterns of communication and
interaction behaviors of the
instructor; And the last dimension, the privacy and security
14. dimension relates to the
privacy and security measures that can be included in the online
learning system.
Methodology
Survey
After being reviewed and approved by the university’s
Institutional Review Board, a
Web-based survey was conducted to confirm the proposed
framework of trust-
inducing factors. To collect a sample that represents the point of
view from the
students, or the users of online learning, in higher education,
the link to the survey
was distributed to students in a four-year university in the
United States through
Distance Education 349
various methods, including university listservs, online
announcements, and faculty
Twitter posts. To encourage participation, 12 $25 Barnes and
Noble gift cards were
used as incentives given to winners of a random drawing from
the survey respon-
dents. The data collection was anonymous with respondents’
express consent (by
reading the informed consent form and checking the consent
checkbox to proceed to
the next Web page of the survey). The respondents were also
informed that their
personal information (i.e., university email address and ID)
15. would not be linked to
their submitted answers if they wanted to be included in the
prize drawing by enter-
ing their personal information on a separate Web page after
completing the survey.
Table 1. A socio-technical framework of trust-inducing factors
in online learning.
Dimensions Trust-inducing factors Literature sources
Credibility � Prior positive experience
with the online learning
system or the instructor
� Good reputation of the
online learning system or the
instructor
Anwar and Greer (2012); Song
and Zahedi (2007)
Design � High information and design
quality of the online learning
system
� Good accessibility and
usability of content and tools
in the online learning system
� Display of contact details of
the instructor or the physical
entity behind the online
learning system
Bansal, Zahedi, and Gefen (2008);
16. Jaeger and Xie (2009); Nicolaou
and McKnight (2006)
Instructor socio-
communicative
style
� Assertiveness of the
instructor
� Responsiveness of the
instructor
� A sense of care and
community created by the
instructor
Curzon-Hobson (2002); Wooten
and McCroskey (1996)
Privacy and
security
� Disclosure of understandable
and adequate privacy and
security policy statement
� Use of security mechanisms
(e.g., the secure HTTP
protocol, encryption, secured
logging system)
� Compliance with third-party
privacy assurance or
standard (e.g., US-EU &
US-Swiss Safe Harbor
17. Frameworks, IEEE LTSC)
� Reliable and timely access to
the online learning system
Akhter, Buzzi, Buzzi, and
Leporini (2009); Bansal et al.
(2008); El-Khatib et al. (2003);
Raitman, Ngo, Augar, and Zhou
(2005)
350 Y. D. Wang
The initial survey was first reviewed by four student counselors
and one lan-
guage expert for consistency, completeness, and readability.
The objective of this
step was to examine the validity of each item in the survey. As
a result, several
items were reworded to improve readability and clarity.
The resulting survey is described as follows. After the
aforementioned
informed consent form, the first section of the survey consisted
of four radio-
button groups gathering demographic information on a
respondent’s class level,
gender, weekly hours spent on the Internet, and experience with
taking an online
course. The second section of the survey included 12 items to
rate, which corre-
sponded to the 12 trust-inducing factors in the proposed
framework. Respondents
rated each item using a 10-point Likert-type scale, which
18. allowed them to select
a response indicating the trust-inducing importance of each
factor. The scale
anchors ranged from 1, representing that the factor was not
important at all, to
10, indicating that the factor was extremely important. The third
section con-
sisted of four questions that are only relevant for students with
disabilities. The
last section of the survey was a feedback box providing for
comments. As men-
tioned previously, if the respondent wanted to be included in the
prize drawing,
he or she could provide personal information on a separate Web
page following
the survey.
Respondents
Although 398 students responded to the Web-based survey, a
total of 361 respon-
dents were included in the final analysis; the other 37 students
were eliminated due
to incomplete submissions. Table 2 presents the characteristics
of the participants,
based upon the information reported on the survey. Since the
survey did not target
specific individuals, there is no response-rate calculation. In
addition, this approach
did not yield a truly random sample from a population, but it
did produce a repre-
sentative pool of university students.
Among the respondents, 170 students (47%) were female, and
243 (67%) students
reported that they had taken an online course. Most of them
19. were undergraduate
students (n = 221, 61%), and the rest were graduate and doctoral
students. The large
majority of the respondents were experienced with the Internet
(n = 325, 90% spent
more than 10 h per week online; n = 216, 60% spent more than
20 h per week online).
Table 2. Characteristics of survey respondents (N = 361).
Gender Online learning experience
Female 170 Yes 243
Male 191 No 118
Class level Weekly Internet hours
Freshman 35 1–10 h 36
Sophomore 34 11–20 h 109
Junior 87 21–30 h 100
Senior 65 >30 h 116
Graduate student 129
Other 11
Distance Education 351
Statistical analyses and results
To meet the research goals of the study, the data analysis had
four parts: (1) validat-
ing the proposed framework of trust-inducing factors and
confirming the underlying
dimensions; (2) evaluating the magnitudes of the ratings across
the confirmed
20. dimensions; (3) comparing trust ratings based on demographic
and experiential sub-
groups; and (4) investigating feedback from students with
disabilities.
Validating the proposed framework
To create the classification of the four dimensions, the author
initially applied a
semantic grouping of the factors obtained from the literature.
Additionally, the 12
trust-inducing factors were subjected to a confirmatory factor
analysis (CFA) to
assess the construct validity and internal reliability of the
constructs. CFA is a pow-
erful statistical tool for examining the nature of and relations
among latent con-
structs, for example, attitudes, traits, intelligence, and clinical
disorders (Jackson,
Gillaspy, & Purc-Stephenson, 2009). In the present study, it was
used to validate the
trust-inducing factors and determine the essential dimensions of
the identified fac-
tors. Before a CFA could be applied, however, two tests that
indicated the suitability
of the data for structure detection had to be run: The extremely
high value (.908)
from the Kaiser-Meyer-Olkin test, which measures sampling
adequacy, indicated that
a factor analysis would be useful with the data. The significant
Bartlett’s test
(p < .001), which examines whether the variables are related,
indicated that the data
were suitable for structure detection. Therefore, a CFA was
performed.
21. The principal components analysis was used to analyze the raw
matrix of 361
responses with the latent root criterion (eigenvalue = 1).
Surprisingly, there were
only two components with eigenvalues greater than 1 (i.e.,
eigenvalue = 6.65 and
eigenvalue = 1.44); these two components accounted for 67% of
the total variance
of the data-set. The screen test, which showed that there were
some bending points
at two components, further verified the number of dimensions.
Based on this initial
analysis, the author tried several rotation methods to determine
which factors loaded
on each of the two dimensions. The Varimax rotation method,
which best revealed
the underlying relationship, was chosen eventually. As can be
read from Table 3, all
factor loadings reach the acceptable level of .3 (Nunnally,
1978), with most of them
exceeding .7. This means that no factor in the proposed
framework should be elimi-
nated since every item fit into one of the two components (all
factor loadings ≥.30).
The analysis also showed that the items for each component
loaded unambiguously.
The major difference between the analysis result and the
proposed model was
the number of dimensions: the 12 factors clustered into two
components rather than
four. Closer investigation indicated that the second component
actually included all
three factors but the last one in the privacy and security
dimension, and the first
component included all the other factors. Thus, the author
22. named the first compo-
nent the course instruction dimension, which related to different
aspects (e.g., repu-
tation, design quality, and instructor socio-communicative
style) of the online
course, and kept the last component as the privacy and security
dimension. To
examine the internal reliability of each dimension (i.e., course
instruction, privacy,
and security), Cronbach’s alpha was calculated on each
dimension, and the alpha
coefficients were .91 and .90, respectively. According to
Nunnally (1978), an alpha
of .50 or higher indicates a sufficient level of internal
reliability. Therefore, based on
352 Y. D. Wang
these results, it may be concluded that these two dimensions
represented different
aspects or features of an online course or environment to
promote student trust.
Evaluating relative importance of dimensions
To investigate the relative magnitudes in ratings among the
survey items that fell
within each of the two dimensions, the median rating across
those items was deter-
mined for each of the 361 respondents. The median is the
appropriate index of cen-
tral tendency for ordinal data (Sermeus & Delesie, 1996).
Figure 1 presents boxplots
of the medians of those ratings for each of the two dimensions.
23. It shows that both
medians exceed 5, but the median for the course instruction
dimension is higher
than that for the privacy and security dimension (9 vs. 8). The
results of the
Kruskal-Wallis test with pairwise comparisons show significant
difference between
the two dimensions (χ2 = 14.33, df = 1, p < .001). This data
suggests that every
Table 3. Rotated component matrix of the trust-inducing factors
(N = 361).
Dimensions Features
Component
1 2
Course instruction C1 – Prior positive experience .515
C2 – Good reputation .733
C3 – High information and design quality .894
C4 – Contact details .810
C5 – Instructor assertiveness .632
C6 – Instructor responsiveness .599
C7 – A sense of care and community .801
C8 – Reliable and timely access .760
Privacy and security P1 – Privacy and security policy statement
.835
P2 – Security mechanisms .897
P3 – Third-party privacy assurance or standard .883
Figure 1. Boxplot of the median ratings of the items within each
dimension (the circles are
outliers).
24. Distance Education 353
factor contributed to the value of the respondents’ evaluations,
but the privacy and
security dimension was rated as slightly less important than the
course instruction
dimension.
Comparing demographic and experiential subgroups
Based on the different characteristics of the respondents, the
average ratings of the
12 trust-inducing factors were compared. The purpose of this
part of the analysis
was to investigate whether demographic characteristics and
individual experiences
(i.e., gender, class level, weekly hours spent on the Internet,
and online learning
experience) are related to students’ overall ratings of the trust-
inducing factors in the
socio-technical framework under consideration.
The Kruskal-Wallis test was conducted and showed no
significant difference
between female (n = 170) and male (n = 191) respondents in
their average trust rat-
ings (χ2 = 1.64, df = 1, p = .20). A comparison among the six
class-level categories
(i.e., freshman, sophomore, junior, senior, graduate student, and
other) was not sig-
nificant (χ2 = 4.68, df = 5, p = .46). The respondents selected
one of four categories,
based on their reported weekly hours spent on the Internet (i.e.,
25. 1–10 h, 11–20 h,
21–30 h, and >30 h). The result of the Kruskal-Wallis test
across the four time inter-
vals was not significant (χ2 = .93, df = 3, p = .82). A
comparison was also made
between the respondents who reported previous online learning
experience (i.e., took
at least an online course) (n = 243) and those who did not (n =
118). There was no
significant difference in trust ratings between the two subgroups
(χ2 = .029, df = 1,
p = .865). Therefore, the results indicate that the demographic
characteristics and
individual experiences under investigation do not have a
correlation to students’
overall ratings in the survey.
Investigating feedback from students with disabilities
Out of the 361 respondents, 15 students (9 females and 6 males)
identified them-
selves as having one or more disabilities and answered
additional questions in the
survey regarding self-disclosure and trust in online learning
environments. Table 4
shows that the most common disability (60%) noted by the
students was attention
deficit disorder/attention deficit hyperactivity disorder,
followed by learning disabil-
ity (33%). Out of the 15 students, 7 (47%) reported more than
one disability for a
total of 35 disabilities. They were enrolled in a variety of class
levels in both under-
graduate and graduate schools.
As explained previously, a disabled student is required to send a
26. formal request
in order to receive the necessary services or accommodations in
an online course. At
the university where the study took place, this is initiated by a
disabled student sub-
mitting the faculty contact sheet (FCS), which is issued from
the Office of Disability
Services and discloses the type(s) of disability that the student
has and the specific
accommodations that the student needs, to the instructor of the
online course. Out of
the 15 students with disabilities, 10 students (67%) indicated
that they would pro-
vide the FCS to the instructor only when they need
accommodations, and the other
5 students (33%) indicated that they would do so during the first
week of the
semester or before class starts. In responding to the question ‘If
an online course is
perceived to be trustworthy to you, will you provide the Faculty
Contact Sheet to
your instructor before or during the first week of the semester?’,
11 students (73%)
354 Y. D. Wang
indicated ‘yes’, including 6 out of the 10 students who only
wanted to provide the
FCS to the instructor as needed. Therefore, the results show that
trust, or perceived
trustworthiness of an online course, does have a positive
influence on the level of
self-disclosure of students with disabilities.
27. Discussion
In this article, the underlying dimensions of the proposed socio-
technical framework
of trust-inducing factors were confirmed, and the relative
magnitudes of respon-
dents’ ratings of the confirmed dimensions were further
evaluated. The results of the
CFA suggest that two underlying dimensions, course instruction
and privacy and
security, exist among the 12 trust-inducing factors. Although all
12 factors were
found to contribute to the respondents’ perception of the
trustworthiness of an online
course, the two identified dimensions differed in terms of their
relative importance
to inducing student trust. The course instruction dimension was
rated about 10%
higher than the privacy and security dimension. This suggests
that the social and
course design factors (e.g., reputation, design quality, and
instructor socio-communi-
cative style), when used effectively, can help overcome
students’ privacy and secu-
rity concerns for an online course.
A further step was taken to investigate whether demographic
characteristics and
individual experiences (i.e., gender, class level, weekly hours
spent on the Internet,
and online learning experience) were related to students’
average ratings of the 12
trust-inducing factors. The results show that none of the
demographic and experien-
tial factors have significant relationships with trust ratings in
the current study. The
28. lack of significant differences in the results might be
attributable to the sample size
and the similar background of the respondents, who are all
students in a four-year
university in the US. In addition, a small number of the
respondents might be unfa-
miliar with some of the terminology (e.g., security protocols or
mechanisms) used in
the trust-inducing factors to assess. More explicit examples or
visual demonstration
of features can be used to assist respondents in assessing the
perceived trustworthi-
ness of certain factors, and thus, more thorough examination
and elaboration of the
results may be needed in the future.
Finally, the feedback from 15 students with disabilities was
investigated. The
majority of the students initially held reservations against
disclosing their disabilities
to, and requesting accommodations from, the instructor in an
online course before
the class starts or during the first week of the semester;
however, should the online
course be perceived to be trustworthy, 60% of these students
indicated their desire
to withdraw their reservations and reach out to the instructor.
This shows that trust,
Table 4. Percentage of respondents indicating various
disabilities/impairments (N = 15).
Disability/Impairment Percentage of respondents (%)
ADD/ADHD 60
Learning disability 33
29. Mobility 20
Medical impairment 20
Speech and language impairment 13
Deaf and hard of hearing 13
Asperger/Autism 13
Psychological/Emotional impairment 13
Distance Education 355
or perceived trustworthiness of an online course, does have a
positive influence on
the level of self-disclosure of students with disabilities. Despite
the small sample
size, this observation has important implications for our
understanding of disabled
students’ acceptance for online learning.
The results of the survey are supportive of implementation of
trust-inducing fac-
tors in all aspects of an online learning environment, including
the delivery system
or platform, the course content, and the instructor. Instructional
designers and online
instructors would be well advised not to neglect the
contributions of all these
aspects, as these social and technical factors act together to
promote student trust.
There was no direct comparison among the trust-inducing
factors in the framework
with respect to which factor has the highest strength of
correlational relationship
with the overall perceived trustworthiness of an online course.
Rather, we suggest
that all factors serve as antecedent variables that might be
30. influential in the rated
ingredients constituting the dimensions, with the course
instruction dimension show-
ing the most robust correlational relationship with the students’
trust ratings.
Conclusion
As online learning continues to gain widespread attention and
thrive as a legitimate
alternative to classroom instruction, educational institutions,
and online instructors
face the challenge of building and sustaining student trust in
online learning environ-
ments. The present study represents an attempt to address the
challenge by identify-
ing the social and technical factors that can likely induce or
influence students’
perception about the trustworthiness of an online course and
integrating the factors
into a socio-technical framework that can be empirically
validated.
The contributions that the present study brings to the research
field are threefold.
First, the study identifies 12 trust-inducing factors from the
literature and provides
empirical evidence and indicative support for their importance
in affecting students’
trust in online learning. It fills the gap in research by focusing
on the antecedents or
determinants of student trust in online learning environments.
Second, the study
extends the CFA to a new application area of trust evaluation in
online learning.
Although online learning evaluation has been traditionally
31. limited to the evaluation
of teaching effectiveness, CFA offers an accessible analysis
method for researchers
to investigate and promote online learning from a unique angle.
Last, but not least,
the results of the study contribute to the growing literature,
which suggests that trust
is a precondition for disclosing of sensitive information by
students with disabilities
not only in face-to-face situations but also in online
environments. By implementing
strategies and features that enhance the trustworthiness of
online learning environ-
ments, online instructors can be more effective in meeting their
responsibilities
under the law by practicing inclusive instruction and helping
students with disabili-
ties succeed in online learning.
One line of future research is in relation to the additional
factors that could be
continuously added to the socio-technical framework. For
example, the respondents
in the survey have repeatedly identified that the inclusion of a
face-to-face opportu-
nity and interactive social media could help foster their trust in
an online course, in
addition to the identified trust-inducing factors. Future research
may continue to val-
idate the framework in a more controllable experimental setting
and examine the
issues of online trust in regard to gender, ethnicity, or culture.
Another aspect of
trust that can be worthy of investigation is the student-to-
student trust in the online
32. 356 Y. D. Wang
learning context, as the present study exclusively focuses on
student trust in an
online course, including the e-learning system, the instructor or
the educational insti-
tution. With the increasing use of social media and tools for
collaborative learning
in online courses, the interaction and trust between students
may play a role in the
course’ sustainability and the students’ performance.
Future research may also explore the intersection of online
learning and disabil-
ity in depth, especially investigating how to establish a
trustworthy online learning
environment for students with disabilities, and ultimately
improving the online learn-
ing experience of students with a wide variety of barriers.
Acknowledgments
Funding for this research was provided by the Office of
Distance Education, George Mason
University.
Note
1. A briefer version of the framework was presented at the 15th
Annual ATINER Interna-
tional Conference on Education, 2013, and appeared within the
proceedings.
Notes on contributor
Ye Diana Wang is an associate professor in the Department of
33. Applied Information Technology
at George Mason University. She has taught information
technology (IT) for over 10 years, at
undergraduate and graduate levels and in classroom and online
settings. Her recent research
focus is on pedagogy, IT education, and distance education.
References
Akhter, F., Buzzi, M. C., Buzzi, M., & Leporini, B. (2009).
Conceptual framework:
How to engineer online trust for disabled users. In P. Boldi, G.
Vizzari, G. Pasi, &
R. Baeza-Yates (Eds.), Proceedings of the 2009
IEEE/WIC/ACM International Joint
Conference on Web Intelligence and Intelligent Agent
Technology (Vol. 3, pp. 614–617).
New York, NY: IEEE.
Al-Ani, B., Redmiles, D., de Souza, C. R., Prikladnicki, R.,
Marczak, S., Lanubile, F., &
Calefato, F. (2013). Trust in virtual teams: Theory and tools. In
A. Bruckman & S.
Counts (Eds.), Proceedings of the 2013 Conference on
Computer Supported Cooperative
Work Companion (pp. 301–306). New York, NY: ACM.
Allen, I. E., & Seaman, J. (2013). Changing course: Ten years
of tracing online education in
the United States. San Francisco, CA: Babson Survey Research
Group and Quahog
Research Group LLC.
Anwar, M., & Greer, J. (2012). Facilitating trust in privacy-
preserving e-learning environ-
ments. IEEE Transactions on Learning Technologies, 5, 62–73.
34. doi:10.1109/TLT.2011.23
Bansal, G., Zahedi, F., & Gefen, D. (2008). The moderating
influence of privacy concern on
the efficacy of privacy assurance mechanisms for building trust:
A multiple-context
investigation. Proceedings of the Twenty-ninth International
Conference on Information
Systems (pp. 1528–1546). Atlanta, GA: Association for
Information Systems.
Bell, B. S., & Federman, J. E. (2013). E-learning in
postsecondary education. The Future of
Children, 23, 165–185. doi:10.1353/foc.2013.0007
Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A.,
Tamim, R. M., Surkes, M. A.,
& Bethel, E. C. (2009). A meta-analysis of three types of
interaction treatments in dis-
tance education. Review of Educational Research, 79, 1243–
1289. doi:10.3102/
0034654309333844
Distance Education 357
http://dx.doi.org/10.1109/TLT.2011.23
http://dx.doi.org/10.1353/foc.2013.0007
http://dx.doi.org/10.3102/0034654309333844
http://dx.doi.org/10.3102/0034654309333844
Bowker, N., & Tuffin, K. (2002). Disability discourses for
online identities. Disability and
Society, 17, 327–344. doi:10.1080/09687590220139883
Briggs, P., Simpson, B., & Angeli, A. D. (2004).
35. Personalisation and trust: A reciprocal rela-
tionship? In C. Karat, J. O. Blom, & J. Karat (Eds.), Designing
personalized user experi-
ences in eCommerce (pp. 39–55). Dordrecht: Kluwer Academic.
Bulach, C. (1993). A measure of openness and trust. People and
Education, 1, 382–392.
Carchiolo, V., Correnti, D., Longheu, A., Malgeri, M., &
Mangioni, G. (2008). Reliable per-
sonalized learning paths: the contribution of trust to e-learning.
In M. D. Lytras, J. M.
Carroll, E. Damiani, R. D. Tennyson, D. Avison, G. Vossen, &
P. Ordóñez de Pablos
(Eds.), The open knowledge society: A computer science and
information systems mani-
festo (pp. 221–228). Berlin: Springer.
Carchiolo, V., Longheu, A., & Malgeri, M. (2010). Reliable
peers and useful resources:
Searching for the best personalised learning path in a trust-and
recommendation-aware
environment. Information Sciences, 180, 1893–1907.
doi:10.1016/j.ins.2009.12.023
Cheung, M. K., & Lee, M. K. O. (2006). Understanding
consumer trust in Internet shopping:
A multidisciplinary approach. Journal of the American Society
for Information Science
and Technology, 57, 479–492. doi:10.1002/asi.20312
Curzon-Hobson, A. (2002). A pedagogy of trust in higher
learning. Teaching in Higher Edu-
cation, 7, 265–276. doi:10.1080/13562510220144770
El-Khatib, K., Korba, L., Xu, Y., & Yee, G. (2003). Privacy and
36. security in E-learning. Inter-
national Journal of Distance Education Technologies, 1, 1–19.
doi:10.4018/
jdet.2003100101
Ghosh, A. K., Whipple, T. W., & Bryan, G. A. (2001). Student
trust and its antecedents in
higher education. Journal of Higher Education, 72, 322–340.
doi:10.2307/2649334
Grandison, T., & Sloman, M. (2000). A survey of trust in
Internet applications. Communica-
tions Surveys & Tutorials, IEEE, 3, 2–16.
doi:10.1109/COMST.2000.5340804
Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance:
Antecedents in a technology
acceptance model. Journal of Business Research, 62, 565–571.
doi:10.1016/j.jbus-
res.2008.06.016
Hashem, M. E. (2011). The role of new information technology
in education and develop-
ment: A case study of building trust in distance education.
International Journal of Arts
& Sciences, 4, 387–398.
Jackson, D. L., Gillaspy, J. A., & Purc-Stephenson, R. (2009).
Reporting practices in confir-
matory factor analysis: An overview and some
recommendations. Psychological Methods,
14, 6–23. doi:10.1037/a001469
Jaeger, P. T., & Xie, B. (2009). Developing online community
accessibility guidelines for
persons with disabilities and older adults. Journal of Disability
37. Policy Studies, 20, 55–63.
doi:10.1177/1044207308325997
Jairak, R., Praneetpolgrang, P., & Mekhabunchakij, K. (2009).
An investigation of trust in
e-learning for instructors and students in private and public
universities. In S. Charmonman
(Ed.), Proceedings of the 6th eLearning for Knowledge-Based
Society Conference
(pp. 17–18). Bangkok: Assumption University Press.
Jarvenpaa, S. L., Tractinsky, N., & Vitale, M. (2000). Consumer
trust in an internet store.
Information Technology and Management, 1, 45–71.
doi:10.1111/j.1083-6101.1999.
tb00337.x
Kramer, R. M. (1999). Trust and distrust in organizations:
Emerging perspectives, enduring
questions. Annual Review of Psychology, 50, 569–598.
doi:10.1146/annurev.psych.
50.1.569
Liu, Y., Chen, D., & Sun, J. (2011). A trustworthy e-learning
based on trust and quality eval-
uation. Proceedings of 2011 International Conference on E-
Business and E-Government
(pp. 1–4). New York, NY: IEEE.
Liu, Y., & Wu, Y. (2010). A survey on trust and trustworthy e-
learning system. In F. L.
Wang, Z. Gong, X. Luo, & J. Lei (Eds.), Proceedings of 2010
International Conference
on Web Information Systems and Mining (pp. 118–122). Berlin:
Springer-Verlag.
38. Luhmann, N. (2000). Familiarity, confidence, trust: problems
and alternatives. In D. Gambet-
ta (Ed.), Trust: Making and breaking cooperative relations, 6
(pp. 94–107). New York,
NY: Basil Blackwell.
358 Y. D. Wang
http://dx.doi.org/10.1080/09687590220139883
http://dx.doi.org/10.1016/j.ins.2009.12.023
http://dx.doi.org/10.1002/asi.20312
http://dx.doi.org/10.1080/13562510220144770
http://dx.doi.org/10.4018/jdet.2003100101
http://dx.doi.org/10.4018/jdet.2003100101
http://dx.doi.org/10.2307/2649334
http://dx.doi.org/10.1109/COMST.2000.5340804
http://dx.doi.org/10.1016/j.jbusres.2008.06.016
http://dx.doi.org/10.1016/j.jbusres.2008.06.016
http://dx.doi.org/10.1037/a001469
http://dx.doi.org/10.1177/1044207308325997
http://dx.doi.org/10.1111/j.1083-6101.1999.tb00337.x
http://dx.doi.org/10.1111/j.1083-6101.1999.tb00337.x
http://dx.doi.org/10.1146/annurev.psych.50.1.569
http://dx.doi.org/10.1146/annurev.psych.50.1.569
Nicolaou, A. I., & McKnight, D. H. (2006). Perceived
information quality in data exchanges:
Effects on risk, trust, and intention to use. Information Systems
Research, 17, 332–351.
doi:10.1287/isre.1060.010
Nunnally, J. C. (1978). Psychometric theory (2nd ed.).
Englewood Cliffs, NJ: McGraw-Hill.
O’Brien, B. S., & Renner, A. L. (2002). Online student
retention: Can it be done? In
39. P. Barker & S. A. Rebelsky (Eds.), Proceedings of the World
conference on educational
multimedia, hypermedia and telecommunications (pp. 1479–
1483). Waynesville, NC:
Association for the Advancement of Computing in Education.
Patterson, B., & McFadden, C. (2009). Attrition in online and
campus degree programs.
Online Journal of Distance Learning Administration, 12.
Retrieved from http://www.west
ga.edu/~distance/ojdla/
Pelet, J., & Papadopoulou, P. (2012). Investigating the effect of
color on memorization and
trust in e-learning: The case of KMCMS.net (knowledge
management and content man-
agement system). In Information Resources Management
Association (Ed.), Organiza-
tional learning and knowledge: Concepts, methodologies, tools
and applications (pp.
1501–1527). Hershey, PA: Business Science Reference.
Raitman, R., Ngo, L., Augar, N., & Zhou, W. (2005). Security
in the online e-learning envi-
ronment. In P. Goodyear, D. Sampson, D. Jin, T. Yang,
Kinshuk, T. Okamoto, R. Hartley,
& N.-S. Chen (Eds.), Proceedings of the Fifth IEEE
International Conference on
Advanced Learning Technologies (pp. 702–706). Washington,
DC: IEEE Computer Soci-
ety.
Ridings, C. M., Gefen, D., & Arinze, B. (2002). Some
antecedents and effects of trust in vir-
tual communities. The Journal of Strategic Information
40. Systems, 11, 271–295.
doi:10.1016/S0963-8687(02)00021-5
Sermeus, W., & Delesie, L. (1996). Ridit analysis on ordinal
data. Western Journal of Nurs-
ing Research, 18, 351–359. doi:10.1177/019394599601800309
Sims, R. (2008). Rethinking (e)learning: A manifesto for
connected generations. Distance
Education, 29, 153–164. doi:10.1080/01587910802154954
Song, J., & Zahedi, F. (2007). Trust in health infomediaries.
Decision Support Systems, 43,
390–407. doi:10.1016/j.dss.2006.11.011
Steel, J. L. (1991). Interpersonal correlates of trust and self-
disclosure. Psychological
Reports, 68, 1319–1320. doi:10.2466/PR0.68.4.1319-1320
Tyler-Smith, K. (2006). Early attrition among first time
eLearners: A review of factors that
contribute to drop-out, withdrawal and non-completion rates of
adult learners undertaking
e-Learning programmes. Journal of Online Learning and
Teaching, 2, 73–85. Retrieved
from jolt.merlot.org/
Wang, Y. D., & Emurian, H. H. (2005). An overview of online
trust: Concepts, elements, and
implications. Computers in Human Behavior, 21, 105–125.
doi:10.1016/
j.chb.2003.11.008
Wooten, A. G., & McCroskey, J. C. (1996). Student trust of
teacher as a function of socio-
communicative style of teacher and socio-communicative
41. orientation of student. Commu-
nication Research Reports, 13, 94–100.
doi:10.1080/08824099609362075
Xu, Y., & Korba, L. (2002). A trust model for distributed e-
Learning service control. In
M. Driscoll & T. Reeves (Eds.), Proceedings of World
Conference on E-Learning in
Corporate, Government, Healthcare, and Higher Education (pp.
2419–2434).
Chesapeake, VA: AACE.
Xu, Y., & Korba, L. (2005). Building trust for interactive e-
learning. In C. Howard,
J. Boettcher, L. Justice, K. Schenk, P. Rogers, & G. Berg
(Eds.), Encyclopedia of dis-
tance learning (pp. 192–195). Hershey, PA: Information Science
Reference.
Yang, S. J. H., Chen, I. Y. L., Kinshuk, & Chen, N.-S. (2007).
Enhancing the quality of
e-learning in virtual learning communities by finding quality
learning content and trust-
worthy collaborators. Educational Technology & Society, 10,
84–95. Retrieved from
http://www.ifets.info/
Ziegler, C. N., & Lausen, G. (2004). Analyzing correlation
between trust and user similarity
in online communities. In C. Jensen, S. Poslad & T. Dimitrakos
(Eds.), Proceedings of
the 2nd International Conference on Trust Management (pp.
251–265). Berlin: Springer.
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