This study examines perceptions of success factors in online learning from instructors' perspectives across four universities in different countries. It uses a model that considers input factors related to students, process factors regarding institutional support, and output factors such as knowledge gained. A survey of 322 online instructors identified that: (1) Instructors from Chinese and Mexican universities saw student characteristics as most important. (2) Instructors from an American university perceived institutional support as key. (3) Instructors from a Spanish university reported that learning outcomes mattered most. In general, instructors valued course content, social interaction, instruction quality over technological issues.

1. Open Learning, 2016
VOL. 31, NO. 1, 25–41
http://dx.doi.org/10.1080/02680513.2016.1151350
A cross-national study of teacher’s perceptions of online
learning success
Elena Barberàa
, Pilar Gómez-Reya
and Francisco Fernández-Navarrob
a
eLearn Center, Universidad Oberta de Catalunya, Barcelona, Spain; b
Department of Quantitative Methods,
Universidad Loyola Andalucía, Sevilla, Spain
Introduction
Several theories for analysing factors that determine success in online courses for students
have been suggested by various researchers. Most of these studies only considered the
students’perceptions while ignoring those of the teachers. For instance, according toVolery
and Lord (2000), the three most important variables relating to students’ perceptions to
consider when delivering an online course were the technology, instructor characteristics
and student characteristics. These findings were obtained after conducting an anonymous
questionnaire answered by a total of 47 students enrolled on an online management course
at an Australian university. From a different perspective, Song, Singleton, Hill and Koh (2004)
identified the design of the course, comfort with online technologies and time manage-
ment as helpful components in an online course and the lack of community, difficulty in
understanding instructional goals and technical problems as barriers or challenges to be
addressed in online environments. These conclusions were obtained after conducting a
ABSTRACT
This study examines success factors in online learning from the
instructors’perspective. Academic success comprises not only student
satisfaction and good grades, but also perception of learning and
knowledge transfer. A systemic model of inputs–process–outputs of
learning was used. A total of 322 online teachers from four different
universities and countries were used to study factors of attainment.
Findings suggest that: (i) instructors from the University of Peking and
the Autonomous Popular University of the State of Puebla reported
learnerfactorsasthemostimportantforstudentsononlinecourses,(ii)
instructors from the University of New Mexico perceived institutional
factors as the most important for establishing effective online learning
and (iii) instructors from the Open University of Catalonia reported
outcome factors as the most important for learners in online courses.
Compared with other research results in online learning, instructors in
this study generally reflect a greater concern about the content, social
presence, instruction and their interactions than about technological
matters.
© 2016 The Open University
KEYWORDS
Cultural settings; teacher’s
perceptions; online
education; critical success
factors
CONTACT Pilar Gómez-Rey pgomez_del_rey@uoc.edu

2. 26 E. Barberà et al.
survey among 90 graduate students at their university. In line with previous studies, Selim
(2007) collected data from 900 undergraduate university students provided by the College
of Business and Economics at the United Arab Emirates University. Findings suggested that
critical success factors in online learning can be grouped into four categories: the instructor,
the student, information technology and university support. On the other hand, Paechter,
Maier and Macher (2010) concluded that the course design, interaction between students
and instructor, interaction with peers, individual learning processes and course outcomes
were critical factors in the students’ perceived satisfaction with the online course. Finally,
Kauffman (2015) reviewed a broad range of factors that affect adult learners’performance
and satisfaction in online learning (including learning outcomes, instructional design and
learner characteristics) and concluded that in terms of performance, learners’ emotional
intelligence plays a surprisingly critical role in the success of the online course, whereas
learners’satisfaction is mainly correlated with the interactivity and relevance of the course.
Some research papers also examine the teachers’role as a determining factor in students’
satisfaction with online learning, considering for that purpose the teachers’perceptions (Eom,
Wen, & Ashill, 2006). The main drawbacks of using students’ perceptions for determining
critical success factors in online learning are that they are not subject matter experts and,
therefore, are not in a position to make judgments about the currency or accuracy of the
course and that students’perceptions are sometimes influenced by their own motivations,
attitudes and needs, as suggested in Fox and Hackerman (2002). Furthermore, it is important
to mention that the teacher’s role is significant in all educational fields, but when referring to
online education, this role becomes even more relevant. Indeed, it has been found that what
actually produces success is the combination of teachers and the instruction in the courses
(Zhao et al., 2009). For all the above-mentioned reasons, we have used teachers’perceptions
to determine the critical success factors in online courses in this study.
The literature provides little insight into critical success factors in online learning from the
teachers’perspective. Some examples include the work of McPherson and Baptista Nunes
(2006), which pointed out diverse organisational critical success factors for e-learning and
higher education environments. These authors revealed 66 critical success factors divided
into four categories: (i) leadership, structural and cultural issues, (ii) design issues, (iii) tech-
nological issues and (iv) delivery issues. From another perspective, Sela and Sivan (2009)
carried out a combination of analytical work and qualitative interviews with practitioners to
identify success factors for enterprise-wide e-learning. After analysing the collected data, the
following key factors were found: (i) useful and easy to use e-learning tools, (ii) marketing, (iii)
management support, (iv) the right organisational culture and (v) the existence of a real need
for the organisation as must-have factors; and (vi) time to learn, (vii) support, (viii) mandatory
learning and (ix) incentives as nice-to-have factors. Finally, Bhuasiri, Xaymoungkhoun, Zo,
Rho and Ciganek (2012) asked practitioners using the Delphi method firstly to list important
factors that influence e-learning and secondly to rate those factors.The study by Bhuasiri et
al. (2012) found six critical dimensions for implementing e-learning systems in developing
countries, including learners’characteristics, instructors’characteristics, institution and ser-
vice quality, infrastructure and system quality, course and information quality and extrinsic
motivation.
This research study aims to provide international results regarding the perception
of success in online education from the teachers’ point of view. In line with the current

3. Open Learning 27
literature and previous research, learning success is determined using a model based
on substantial higher education online subject matters. The model was developed by
displaying input, process and output factors (Barbera & Linder-VanBerschot, 2011). Input
factors are those aspects mainly contributed by the student, and which are linked to
the process of learning (motivation for online enrolment, self-efficacy, computer com-
petence level, etc.). Process factors include those aspects contributed by the university
institution (e-learning platform, didactic material, instruction design, etc.). Finally, output
factors are not seen as numbers or grades but instead as qualitative gains at the end
of the process (knowledge acquisition, learner satisfaction and ability to transfer). It is
important to mention that in this study we analyse the main educational variables that
define a complete educational process (including the stage where the students are not
yet enrolled on the course, the variables involved while the student is taking the course
and finally the variables found after the course) unlike state-of-the-art research works
that analyse the success factors in online learning from the instructors’ point of view
and focus on specific parts of the online course. For instance, the study by McPherson
and Baptista Nunes (2006) particularly focused on the impact of organisational values
on the success of the online course and ignored other important learning variables
such as learning content and knowledge acquisition; equally, the research by Bhuasiri
et al. (2012) and Sela and Sivan (2009) neglected variables related to student outcomes.
Furthermore, in line with other research in the literature (for instance, the works of
Bhuasiri et al. [2012] and Magnier-Watanabe, Benton, Herrig, and Aba [2011]), this study
will take into account four culturally different universities to avoid having a bias in the
sample due to the participants’ culture.
To summarise, the main objectives of this study are the following:
Globalaim:To identify global key factors affecting online learning success in higher education
using a systemic model valid for the online learning process.
Cultural aim: To describe the factors that emerge in online education as being culturally
different from the teachers’perspective.
Theoretical framework: systemic model
This section explains the theoretical framework and background knowledge for the factors
and variables included in the Barbera and Linder-VanBerschot (2011) model.The validity and
reliability of the instrument proposed was verified using two surveys which were conducted
in three e-learning universities in three different countries: the Open University of Catalonia
(UOC) in Spain, the University of New Mexico (UNM) in the USA and the University of Peking
(PKU) in China. A sample of 976 and 509 student evaluations from UOC, PKU and UNM was
analysed in the study. The questions in the first and second surveys were grouped accord-
ing to three factors (dimensions): (i) learner factors, (ii) institutional factors and (iii) learning
outcome factors (See Appendix A).The first survey was sent to participants at the beginning
of the course, and the second survey was sent at the end of the course. The high dropout
rates at online universities motivated the use of two surveys instead of a single one at the
end of the course. The aim of the study was to reveal relevant factors for improving online
learning design and e-learning practices.

4. 28 E. Barberà et al.
Learner factors
This dimension includes what students bring to the online learning experience.The learner
factors in this study include the following variables: general self-efficacy (GSE), self-efficacy
online (SEO), motivation (M), prior knowledge (PK) and course expectation (CE). In this con-
text, there are a large number of studies positively associating learner factors with success
and satisfaction in online learning. Significantly, GSE, SEO and M are three of the most rele-
vant components linked to a high level of achievement. Indeed, much of the literature reflects
GSE and SEO as good predictors of satisfaction in face-to-face settings (Lee & Witta, 2001)
and, more specifically, representative authors like Wu, Tennyson and Hsia (2010) associate
a high level of individual computer GSE and SEO with high performance in e-learning. On
the other hand, M in online courses is another key factor directly associated with attainment
and it has been studied for both learners and instructors (Roca & Gagné, 2008). These three
relevant factors (GSE, SEO and M) also comprise a significant interrelationship between them
(Law, Lee, & Yu, 2010). Furthermore, studies of more influential issues in learning processes
and products in general found different characteristics, such as students’ background in
the subject (PK), students’ expectations (CE) of their teachers and vice versa, and teachers
expectations of each other (Chu & Chu, 2010).
Institutional factors
This dimension includes what the university and instructors bring to the learning experience.
The institutional factors in this study encompass the following variables:
•
Learner support (LS). Providing enough support for learners to accomplish tasks suc-
cessfully in online courses is positively associated with their satisfaction.Tanner, Noser
and Totaro (2009) carried out a comparative study of online learners and instructor
assumptions and found that from both sides’ point of view it is important to provide
training, technical support and access to resources.
•
Social presence (SP). This factor refers to the students’ need to feel in communication
with their classmates and recognise them as real people who share common interests
and needs, as well as some online tasks, such as discussion forums. This factor has
traditionally been linked with satisfaction and it could be said that having low SP may
become a problem that leads to bad results and poor learning experiences (Richardson
Swan, 2003).
•
Learningplatform (LP).The e-learning environment has an important role since students
have meaningful educational experiences with well-designed courses and learning
materials and it is important to match the right technology with the right curriculum
and learning objective (Kidd, 2009). Chiu, Chiu and Chang (2007) found that function-
ality, ease of use, reliability, flexibility, data quality, portability and integration all have
a positive effect on learner satisfaction.
•
Instruction (I). Teachers’profiles as well as their knowledge of technology and different
teaching styles for interacting with learners significantly influence e-learning outcomes
(Ozkan Koseler, 2009). Likewise, Oomen-Early and Murphy (2009) observed a strong
influence on satisfaction and perceived effectiveness online when teachers had suffi-
cient, up-to-date knowledge in their area of expertise.

5. Open Learning 29
•
Instructorinteraction (II). Eom et al. (2006) identified a link between learners and instruc-
tor attendance, feedback interaction and perception of knowledge and also found a
significant correlation between facilitation and satisfaction; however, they did not
observe any significant connection to knowledge perception. This study found that
the time that instructors take to reply to queries has a significant influence on student
satisfaction and learning.
•
Learnerinteraction (LI). Swan (2002) found that interaction among students and between
them and teachers influenced students’satisfaction and their perception of knowledge
in the course. Along these lines, LaPointe* and Gunawardena (2004) pointed out that
students who interact more frequently show a high level of satisfaction.
•
Learningcontent (LC). Content should be relevant, yet enticing enough to learners so that
they will retrieve it from the repository, read it, interpret it and then discuss it with other
learners in an interactive online setting. Furthermore, content must also be accessible
to all learners regardless of their connection capabilities. For example, a study linked
to this factor is the work of Levine (2005), who highlighted that content should allow
students to express their interests and interpretations.
•
Coursedesign (CD). Achieving effective online education requires both effective instruc-
tional design and a process involving adequate principles of educational practice. If the
design is correct, it will have a positive influence on instruction. Due to the nature of
online environments, the design of online courses must take into account time flexibility,
location, methods, participation in activities and presentation of the materials, with
the aim of creating a more cooperative learning environment (Simonson, Smaldino,
Albright, Zvacek, 2014).
Learning outcome factors
This dimension refers to the outputs of a learning/teaching experience.The learning outcome
factors considered in this study are:
•
Learnersatisfaction (LST). Satisfaction is one of the most typical measures of effectiveness
in e-learning and is the factor that most frequently arises in the literature as an indica-
tor of student success in e-learning. For instance, Levy and Murphy (2002) stated that
administrators, researchers and teachers should have in-depth knowledge of this factor
in order to ensure the full effectiveness of online courses. From another perspective,
Levy (2007) highlighted that the importance of measuring satisfaction in e-learning is
the major driver of success or failure. He also noted the impact of student satisfaction
on dropout rates from e-learning courses.
•
Knowledge acquisition (KA). This factor refers to the information that students learn on
the course. Linked with this factor is the study by Mayer (2002), which identified two
important educational goals connected to KA (retention and transfer). Moreover, KA is
also connected with instructional design, teaching strategies and enhanced compe-
tences (Sendag Odabas, 2009).
•
Ability to transfer (AT). This factor is defined as the expectation that learners will apply
the knowledge gained in the course to future situations. For example, Mayer (2002)
included transfer as the second of the two most important educational goals. Transfer
takes KA a step further, requiring students to make enough sense of the new information

6. 30 E. Barberà et al.
to apply it to different contexts. The author explained that this leads to a greater sense
of meaningful learning, whereas students collaborate in the construction of knowledge
to solve a problem and make sense of future experiences.
Methodology
Procedures
The research design employed in this study uses both quantitative and qualitative methods.
Two secure online questionnaires with structured questions were administered to instructors
in four different countries (Spain, the USA, China and Mexico).The online questionnaires and
accompanying consent forms were originally written in English and then translated into the
official language(s) of the university by an individual chosen by the researcher representing
the university. The questionnaires were then built using Opinio and hosted on the secure
University of New Mexico Health Sciences application server.The first questionnaire with 15
items was sent to instructors near the beginning of the course. The second questionnaire,
with 39 items, was sent towards the end of the course. A total of 54 items were marked
by teachers. All factors in the questionnaires were scored on a four-point Likert scale. The
complete set of questions (items) defining each educational variable employed in the first
questionnaire (learner factors) and second questionnaire (institutional and learning outcome
factors) as well as the reliability results of each of the educational variables are detailed in
Appendix A.
Participants
Experienced online instructors from four universities constitute the sample of this study.
Firstly, the Open University of Catalonia (UOC) is a fully online university located in Barcelona
(Spain) with around 57,000 students. The mission of the university is to facilitate access to
lifelong learning for adult learners through asynchronous communication tools. Secondly,
the University of New Mexico (UNM), located in Albuquerque, is the largest university in New
Mexico with around 28,000 students in attendance each year. Being close to the Mexican
border, UNM boasts a diverse student population, with 30 per cent of the students being
Hispanic and five per cent being American-Indian. Thirdly, the University of Peking (PKU),
which is a comprehensive and key national university, is the first national university in
modern Chinese history. PKU has over 30,000 students in 31 colleges and 14 departments,
offering 101 undergraduate programmes, 224 postgraduate programmes and 202 doctoral
programmes. Finally, the Autonomous Popular University of the State of Puebla (UPAEP),
located in Mexico, which offers 7 undergraduate programmes as well as 11 postgraduate
programmes. The mission of this university is to educate students in values such as respect
and freedom of human beings.
Specifically, a total of 223 (first survey) and 210 (second survey) full-time teachers lectur-
ing in online social science courses with more than 6 years of teaching experience in online
learning environments were contacted to participate in the study.The sample was purposive
and was selected by the researchers of this study, who contacted the teachers in the four
universities directly to ask them to collaborate in the project. Unfortunately, only 322 out
of the 433 full-time teachers contacted finally decided to take part in the study. Hence, data

7. Open Learning 31
were collected from 322 full-time teachers with the above-mentioned constraints. Of the
total, 192 participants were from UOC (106 teachers answered the first questionnaire and 86
the second one), 25 participants were from UNM (16 teachers answered the first question-
naire and 9 the second one), 25 participants were from PKU (7 teachers answered the first
questionnaire and 18 the second one) and 80 participants were from UPAEP (40 teachers
answered the first questionnaire and 40 the second one). Therefore, the response rate per
university and survey was 70.20 and 65% in UOC, 76.19 and 69% in UNM, 63.63 and 72% in
PKU and 100% for the two surveys in UPAEP.
Analysis
Statistical tests have been applied in order to ascertain the statistical significance of the
differences observed between the mean scores and standard deviations provided by the
instructors of the four universities analysed in the study. Hence, the educational variables
considered are the means (continuous response variable) of ordinal variables (represented
on a four point Likert-scale). In a first phase, the Shapiro–Wilk (S–W) test was used to evaluate
whether the scores provided by the instructors per university followed a normal distribution
(Royston, 1992). For small sample sizes, normality tests have little power to reject the null
hypothesis and therefore small samples most often pass normality tests. The Shapiro–Wilk
(S–W) test is based on the correlation between the data and the corresponding normal scores
and provides better power than the Kolmogorov–Smirnov (K–S) test, even after Lilliefors
correction, particularly when small sample sizes are being tested. In fact, some researchers
recommend the S–W test as the best choice for testing the normality of data in these contexts
(Thode, 2002). The non-parametric Kruskal–Wallis (K–W) one-way analysis of variance test
was conducted to check whether the university being analysed significantly affects the scores
provided by the instructors as the groups studied were small and at least one group out of
the four analysed per educational variable was always non-normal.The K–W test concluded
that these differences were significant (with a p-value = 0.05) in some cases. As we found a
statistically significant result in some educational variables, we needed to compute a post
hoc test and the Mann–Whitney U rank sum test (non-parametric test) post hoc test was
selected.The statistical analysis was finished by applying the Mann–Whitney U rank sum test
(non-parametric test) only to those educational variables where statistical differences were
detected for all pairs of universities. A level of significance of =0:05 was considered, and the
corresponding correction for the number of comparisons was also included.
Results
The material below describes the results of testing the model at the four above-mentioned
universities. The analysis is divided into two parts. In the first part, we analysed the educa-
tional variables as a whole (see Table 1). In the second part (Table 2), the results are divided
by universities and analysed individually. We describe the factors that emerged as being
culturally different and explore trends that suggest likely cultural patterns to be explored
in greater depth in the future. Significant factors are detailed below.
Table 1 shows the mean scores per variable, mean ranking and the outputs of the sta-
tistical tests considered. It is important to mention that none of the educational variables
considered followed a normal distribution and therefore, statistical validation was provided

8. 32 E. Barberà et al.
by non-parametric tests. The Kruskal–Wallis H-test showed that there were statistically sig-
nificant differences in the Learner Factors, Institutional Factors and the overall dimensions
considered. As can be seen in Table 1, overall institutional and learning outcome factors
score significantly higher than the learner factor. From a teacher’s perspective this finding
shows that although studying in online environments appears to require some previous
experience from students, any online student (either inexperienced or experienced in this
type of environment) can achieve success and satisfaction with the course, thanks to factors
controlled by the institution.
Table 2 shows the statistical results disaggregated by universities. As previously men-
tioned, the Shapiro–Wilk test was used with the signification level equal to 0.10 to evaluate
whether the mean scores provided by the instructors from the different universities followed
a normal distribution. As can be seen from the results inTable 2, a normal distribution cannot
be assumed in any of the educational variables considered because the critical levels, p-val-
ues, were never over 0.05 in all the universities analysed. As a consequence, the non-paramet-
ric Kruskal–Wallis test was selected in order to check whether the university being studied
significantly affects the results obtained in the 16 educational variables considered. The
Kruskal–Wallis H-test showed that there were statistically significant differences in Instruction
Table 1. Statistical analysis per educational variables: mean scores and standard deviations per variable,
mean ranking per variable, p-values of the Shapiro–Wilk test, p-values of the Kruskal–Wallis test and
ordered mean for the statistical Mann–Whitney Wilcoxon test.
a
The variable is not normality distributed (α = 0.05).
b
Significant differences were found for α = 0.05.
LF: Learner factors.
IF: Institutional factors.
LOF: Learning outcomes factors.
Mean scores per
variable Mean ranking per variable S–W K–W
Ranking
μσ ̄
R p-Value p-Value
Learner factors (LF)
GSE 2.840.50
357.05 0.00a
SEO 3.270.47
557.90 0.00a
M 2.970.49
419.53 0.00a
0.00b
SEO M ≥ CE PK ≥ GSE
PK 2.860.39
359.57 0.00a
CE 2.970.42
418.06 0.00a
Institutional factors (IF)
LS 3.130.46
517.91 0.00b
SP 3.300.51
638.63 0.00b
I 3.310.45
643.53 0.00b
LP 3.110.51
511.14 0.00b
0.00b
II ≥ I ≥ LC ≥ SP …
II 3.330.60
685.17 0.00b
SP ≥ LI ≥ CD LS ≥ LP
LI 3.260.63
634.35 0.00b
II CD
LC 3.300.52
639.46 0.00b
CD 3.270.50
622.17 0.00b
Learning outcomes factors (LOF)
LST 3.220.49
235.53 0.00b
KA 3.160.51
220.86 0.00b
0.56 LST ≥ AT ≥ KA
AT 3.210.51
233.61 0.00b
Overall ranking
LF 2.990.26
127.50 0.00a
IF 3.260.36
215.26 0.00a
0.00b
IF ≥ LOF LF
LOF 3.220.43
198.39 0.00a

9. Open Learning 33
Table
2. Statistical
analysis
per
University:
mean
scores
and
standard
deviations
per
University,
mean
ranking
per
University,
p-values
of
the
Shapiro–Wilk
test,
p-values
of
the
Kruskal–Wallis
test
and
ordered
mean
for
the
statistical
Mann–Whitney
Wilcoxon
test.
a
The
variable
is
not
normality
distributed
(α = 0.05).
b
Significant
differences
were
found
α = 0.05.
(1):
UOC;
(2):
UNM;
(3):
PKU;
(4):
UPAEP.
(A) ≥ (B):
University
A
yields
better
results
than
University
B,
but
the
difference
is
not
significant.
(A) (B):
University
A
yields
better
results
than
University
B
with
significant
differences.
The
binary
relation ≥ is
not
transitive.
Mean
scores
per
University
Mean
ranking
per
University
S–W
K–W
Ranking
μ
σ
̄
R
p-Value
p-Value
UOC
UNM
PKU
UPAEP
UOC
UNM
PKU
UPAEP
UOC
UNM
PKU
UPAEP
Learner
factors
(LF)
GSE
2.82
0.41
2.90
0.72
2.85
0.85
2.84
0.55
83.46
93.53
77.21
84.90
0.00
a
0.26
0.14
0.06
0.84
(2) ≥ (4) ≥ (1) ≥ (3)
SEO
3.25
0.40
3.31
0.64
3.19
0.79
3.30
0.49
82.76
90.75
89.07
87.93
0.00
a
0.03
a
0.18
0.00
a
0.87
(2) ≥ (3) ≥ (4) ≥ (1)
M
3.03
0.40
2.79
0.66
3.02
0.47
2.88
0.62
88.81
73.72
83.93
79.61
0.00
a
0.04
a
0.03
a
0.00
a
0.54
(1) ≥ (3) ≥ (4) ≥ (2)
PK
2.86
0.36
2.84
0.45
3.14
0.37
2.85
0.44
83.42
82.44
112.86
85.34
0.00
a
0.02
a
0.00
a
0.00
a
0.46
(3) ≥ (4) ≥ (1) ≥ (2)
CE
3.00
0.40
2.88
0.42
2.85
0.79
2.97
0.42
86.64
74.16
80.57
85.76
0.00
a
0.06
0.07
0.04
a
0.78
(1) ≥ (4) ≥ (3) ≥ (2)
Institutional
factors
(IF)
LS
3.17
0.43
3.33
0.52
2.91
0.51
3.11
0.48
79.59
96.06
58.11
75.65
0.00
a
0.48
0.60
0.63
0.09
b
(2) ≥ (1) ≥ (4) ≥ (3)
(2) (3)
SP
3.33
0.47
3.51
0.52
3.16
0.65
3.26
0.51
78.88
93.94
69.25
72.63
0.00
a
0.05
0.09
0.00
a
0.47
(2) ≥ (1) ≥ (4) ≥ (3)
I
3.35
0.37
3.55
0.40
3.03
0.57
3.28
0.49
80.15
99.17
52.86
74.28
0.00
a
0.12
0.04
a
0.00
a
0.03
b
(2) (1) ≥ (4) (3)
LP
3.11
0.48
3.33
0.60
3.12
0.47
3.05
0.57
76.47
93.72
79.17
73.40
0.00
a
0.23
0.08
0.00
a
0.63
(2) ≥ (3) ≥ (1) ≥ (4)
II
3.46
0.51
3.11
0.88
3.00
0.53
3.24
0.68
85.51
70.61
49.14
72.68
0.00
a
0.10
0.50
0.00
a
0.01
b
(1) ≥ (4) ≥ (2) ≥ (3)
(1) (2)
LI
3.29
0.59
3.44
0.64
3.07
0.71
3.23
0.68
77.60
92.56
65.03
75.71
0.00
a
0.04
a
0.00
a
0.00
a
0.45
(2) ≥ (1) ≥ (4) ≥ (3)
LC
3.34
0.46
3.48
0.66
3.15
0.53
3.25
0.59
78.99
96.72
64.33
73.98
0.00
a
0.02
a
0.13
0.00
a
0.27
(2) ≥ (1) ≥ (4) ≥ (3)
CD
3.31
0.46
3.40
0.64
3.05
0.48
3.27
0.57
78.99
92.78
56.11
78.58
0.00
a
0.06
0.02
a
0.00
a
0.13
(2) ≥ (1) ≥ (4) ≥ (3)
Learning
outcomes
factors
(LOF)
LST
3.31
0.44
3.27
0.50
2.96
0.59
3.13
0.50
83.41
89.89
55.86
69.84
0.00
a
0.00
a
0.04
a
0.02
a
0.04
b
(2) ≥ (1) ≥ (4) ≥ (3)
(1) (3)
KA
3.23
0.41
3.15
0.63
2.97
0.52
3.11
0.64
81.02
79.78
58.94
75.86
0.00
a
0.38
0.57
0.00
a
0.27
(1) ≥ (2) ≥ (4) ≥ (3)
AT
3.25
0.47
3.15
0.67
3.08
0.59
3.19
0.54
79.90
74.89
67.19
75.65
0.00
a
0.71
0.01
a
0.02
a
0.71
(1) ≥ (4) ≥ (2) ≥ (3)

10. 34 E. Barberà et al.
(I), Instructor Interaction (II) and Learner Satisfaction (LST) perceptions between the different
scores provided by the instructors at the different universities analysed in the study.
Learner factors
This group of factors indicates what students bring to classes and shows whether they are
sufficiently prepared and capable of following online courses successfully. According to
the high global results for these factors, online students are adequately prepared in teach-
ers’ opinions. Overall, the factors which students bring to the online learning experience
are, in descending order (according to the ranking): self-efficacy online, motivation, course
expectations, prior knowledge and general self-efficacy. Statistically significant differences
were found in the ranking provided by the teachers in self-efficacy online with respect to
the remaining variables (Table 1).This finding revealed that, from the teachers’perspective,
their students are already immersed in the digital native society, as SEO is ranked first among
all the variables included in the learner factors. Furthermore, it is important to mention that
motivation and efficacy online were the two top-rated factors among the learning factor
variables and these variables are actually two of the most important ones which define the
theoretical level of autonomy. This means that, from the teachers’ perspective, online stu-
dents showed high levels of autonomy, which makes sense given the type of environment
in which they have enrolled. This conclusion is in line with the literature, which outlines the
difference in students’ autonomy when comparing face-to-face and online learning envi-
ronments. While in a physical classroom teachers lead the learning process (for instance,
standing in front of their students and maintaining authority over the blackboard), in an
online learning environment the teacher’s role is by contrast more focused on guiding stu-
dents through the learning process and facilitating the response to information; i.e. less
instructor supervision means more student autonomy (Simonson et al., 2014). According
to the Mann–Whitney statistical test, two groups of variables were detected: the variables
SEO, M and CE de ne the first group, while the second is composed of the variables PK and
CE (Table 1).This finding highlights that teachers firmly believe that their students are eager
to study, are motivated and have technological capabilities, but they are not sure about their
students’background or general self-efficacy.
There are no significant differences between what teachers at UOC, UNM, PKU and UPAEP
reported in the educational variables defining the learner factor (Table 2).These educational
variables were very homogeneously distributed across the four universities, hindering the
possibility of detecting statistically significant differences in the rankings provided. These
results indicate that the profile of students enrolling on an online course is similar regardless
of nationality and, therefore, there are no significant differences between them. Online stu-
dents are adults who voluntary enrol on the online course motivated by their own ambitions
or needs. Finally, all four countries score very similarly (in ranking) in: self-efficacy online
and general self-efficacy (see the p-value of the K–W test in Table 2). Findings suggest that
there is an international consensus on the fact that students are more prepared in their
technological abilities than in their general ones, according to the teachers’point of view.

11. Open Learning 35
Institutional factors
This group of factors covers what the institution provides for the online teaching and learn-
ing process, and this research analyses what teachers’think about this institutional support.
Instructors are directly involved in some of the factors, which brings even more practical
importance to the results. Overall, institutional factors score significantly higher than learner
factors (Table 1). For online learning processes, instructors perceive greater excellence in
instructor interaction, instruction, learning content, social presence, learning interaction and
course design, while learning platform and learning support occupy the bottom positions,
although all of them score above three points. In fact, these two groups of variables were also
detected by the Mann–Whitney Wilcoxon test (Table 1) as groups of significantly different
variables.Thus, teachers believe that factors which rely on instruction are well implemented
in the learning-teaching process, whereas the platform and learning support for this platform
could be improved in future learning experiences. Furthermore, significant differences in
rankings were also detected among II and CD. This finding suggests that teachers involved
in this study are more satisfied with variables related to the instructor interaction (II) than
with those linked to course design (CD). It is likely that the universities studied were focused
on providing quality education and therefore neglected other aspects related to the design
of the course. It is important to stress that one of the factors that drives a successful online
learning experience is the course design (Sun, Tsai, Finger, Chen, Yeh, 2008).
The results are consistent, in general, as the UNM teachers tended to unanimously agree
more with the statements than their UOC, UPAEP and PKU peers, while PKU teachers were
the ones who tended to score lower, while never scoring lower than 2.9 points. All four coun-
tries score very similarly (in ranking) in: learning platform, social presence, learning content
and learner interaction (see the p-value of the K–W test inTable 2). On the other hand, there
were significant differences between the four countries in instruction, instructor interaction
followed by learner support. Firstly, with regards to the LS variable, there are significant
differences between teachers from UNM and teachers from PKU. Secondly, concerning the
I variable, a significant number of PKU teachers believe that they provided worse instruction
than the rest of their peers. Finally, UOC teachers think that their II is significantly better than
that provided by teachers at PKU and UNM.
Outcome factors
In this model, online learner success is summarised in outcome factors that are described
as an assemblage of three elements represented by: learner satisfaction with the learning
experience, knowledge acquisition that represents cognitive attainment in the learning pro-
cess and the ability of transfer knowledge to others or other contexts that involves lasting
steps in real learning from a socio-constructivist perspective. No significant differences in
rankings were detected in the educational variables composing the learning outcome factor.
Hence, learning outcome is perceived as a globally constructed factor composed of these
three variables. This is also consistent with the reliability results reported in Appendix A,
where the learning outcome factor was presented as the most reliable factor according to
the results of the Cronbach’s alpha.
Of the three outcome factors in the model, there were remarkable differences only in the
scores provided for learner satisfaction. Specifically, UOC and UMN teachers firmly believe

12. 36 E. Barberà et al.
that, after taking the course, their students are more satisfied than the ones from PKU.
Although there were no drastic differences between universities, UOC teachers tended to
score higher in the statements of these factors than teachers from UNM, UPAEP and PKU
(in descending order).
Discussion and conclusions
Considering the exploratory cultural approach of the application of the model, the findings
lead to some preliminary conclusions that together highlight several key issues to be used
in e-learning in order to really teach internationally. Some preliminary conclusions are:
•
Teaching approaches, which are culturally driven. Reading the results transversally
across factors and countries, it can be said that each group of instructors from each
university stress one of the factors: while PKU obtains a competitive ranking according
to the Mann–Whitney U test in learner factors if compared with the other universities
considered, UNM achieves the best ranking in institutional factors and UOC acquires the
best ranking in outcome factors. As this fact occurs homogenously for each university
it may indicate a different online teaching approach in each country. That is: (a) PKU
shows a tendency towards an individual approach based more on the learner because
of the important weight the learner variables bring to the online learning experience
according to their instructors; (b) UNM displays a trend focused on learning support
that seems to point to a shared approach to teaching and learning because the results
imply instructional issues and a different kind of learner support; and, (c) UOC shows an
inclination towards results by scoring high in outcome factors, which seems to indicate
a need to demonstrate equal achievements for online and face-to-face universities.
•
Learners’background. Learner factors determine what knowledge students already have
and bring to their classes. In this sense, according to the teachers’ answers, students
come to their classes sufficiently prepared (learners factors reached nearly three points
on average, according to teachers’perceptions, despite occupying last place when com-
pared to institutional and learning outcome factors). The sequence of factors in order
is: self-efficacy online, motivation, course expectations, prior knowledge and general
self-efficacy.This indicates that instructors consider their online students as motivated
and reasonably autonomous in the setting.
•
Similar learner factors. As mentioned in Section 4, no significant differences were
detected in the learner factor scores disaggregated by countries, according to teachers’
perceptions. This means that the student profile in online environments seems to be
similar regardless of the country of study. In general terms, we can characterise online
students as adult learners with high levels of motivation and self-efficacy online and,
consequently, with high levels of autonomy.
•
Technology enhances learning. With regards to institutional factors, teachers indicated
instructor interaction, instruction, learning content, social presence, learning interac-
tion and course design as factors that should be critically considered because they are
important for all four universities, despite appearing in different positions. In contrast
to the general concern about technology being first (following several conditions) and
in some situations helping students learn better, the learning platform and its support
for students does not seem to be a priority for instructors in terms of learning success

13. Open Learning 37
and a more general approach based on instruction and content emerges instead.These
technical matters are in line with our findings, which shown that from the teachers’per-
spective, students are already immersed in the digital native society and, therefore, they
do not believe that much technological support is required in their learning process.
•
Similar outcome factors. Although some of the variables used may be different, there
is great consistency in the final results on online educational processes, with similar
scores on outcome factors for all four universities. One prospect for future research is
to compare instructors and learner outcomes to identify similarities and differences
and re ne the findings of this study.
Additionally, although this study performed an analysis of instructors’assumptions in four
different countries, it has some limitations which also represent opportunities for further
research. This study used a sample of learners in the USA, China, Spain and Mexico, but it
could be carried out with samples from other contexts, cultures or countries to identify,
confirm and contrast differences between cultures. In addition, due to the fact that we
have used an instrument with quantitative data, these results could be included in other
instruments to obtain qualitative data and interpret it to see whether there are differences.
In line with cross-cultural literature (Rienties Tempelaar, 2013; Van de Vijver Leung,
1997; Vijver Leung, 2010), the differences analysed could be due to a set of explanatory
reasons, some related to the culture of the organisation, some due to the context these
organisations are working in and some due the (national) culture.Therefore, another poten-
tial limitation of the study is the difficulty in identifying the sources that might explain the
differences detected. Furthermore, our study analysed the period of time in a course, while
future studies should incorporate instructors’and learners’assumptions in different periods
of time by carrying out a time series analysis of different courses and time performance
regarding learners’and instructors’development in an institution. Nevertheless, the findings
clearly point out the relevance of the context in which learning takes place, the context
being even more crucial in online settings where students may be from all over the world.
For that reason, educational researchers who frame their studies within a single cultural
context should be cautious and demonstrate cultural awareness in their results. In the near
future, we should be able to better appreciate the results of the impact of globalisation
in educational environments. Thus, this study could be useful for instructors who want to
provide culturally sensitive learning.
Acknowledgements
The authors are grateful to the researchers, instructors and learners at the University of New Mexico,
the University of Peking and the Open University of Catalonia for participating in this study.The authors
would also like to acknowledge Dr JenniferVanBerschot from University of Colorado and Dr Armando
Cortes from Universidad de Tecmilenio for their prior contribution in related researches. The research
work of F. Fernandez-Navarro was partially supported by the TIN2014-54583-C2-1-R project of the
Spanish Ministry of Economy and Competitiveness (MINECO), FEDER funds and the P2011-TIC-7508
project of the‘Junta de Andalucia’(Spain).
Disclosure statement
No potential conflict of interest was reported by the authors.

14. 38 E. Barberà et al.
Notes on Contributors
Elena Barberà is a PhD in Educational Psychology (1995) and a senior researcher at eLearn
Center (Open University of Catalonia, Barcelona). She is currently Director of the PhD pro-
gramme Education and ICT at OUC. Her research activity is focused in the area of educational
psychology. As head of the e-DUS (Distance School and University e-ducation) research
group, she currently participates in national and international projects and she is an external
evaluator of national and European research projects. She also is the editor of two journals
of impact in the field of education and technology.
Pilar Gómez-Rey received the MSc degree in Business Administration from University ETEA,
Spain, in 2012 and the MSc degree in Teaching Economics for Pre-Higher Education from
the International University of La Rioja, Spain in 2014. Currently she is a PhD candidate at
the Open University of Catalonia where she is developing her thesis through the Doctoral
Programme in Education and ICT (e-learning). Her main research interests include Higher
Education, e-learning, students' perceptions as well as quality education.
Francisco Fernández-Navarro received the MSc degree in computer science from the
University of Cordoba, Spain, in 2008, the MSc degree in artificial intelligence from the
University of Malaga, Spain, in 2009 and the PhD degree in computer science and artificial
intelligence from the University of Malaga in 2011. He was a research fellow in computational
management with the European Space Agency, Noordwijk, The Netherlands and currently
he is working as an associate professor at the Universidad Loyola Andalucia. His current
research interests include neural networks, ordinal regression, imbalanced classification and
hybrid algorithms. He is a member of the IEEE.
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Appendix A. Description of the questionnaire, educational variables used in
this study, factor analysis and reliability results
It is important to mention that all the factors considered (five corresponding to the learners, eight
institutional and three for the learning outcomes) obtained a Cronbach’s alpha above 0.600 except the
8 Motivation factor (M).Therefore, all factors considered obtained either an average reliability or a high
9 reliability (Sekaran Bougie, 2010). Finally, the statement‘Learners need additional motivation from
instructor to complete their tasks’was removed from the statistical analysis as the Cronbach’s alpha is
0.357 if this question is considered and 0.613 if it is finally excluded from the analysis.
As can be seen in Table A.1, it was found that the institutional and learning outcomes dimensions
possess high reliability (in both cases over 0.85). However, the learner factors dimension obtained a
reliability coefficient of 0.579. We hypothesised that this was due to the multi-dimensional nature of
this construct.This hypothesis was confirmed after performing a factor analysis over the learner factor
questionnaire (KMO = 0.665; Bartlett’s sphericity coefficient = 78.434, d.f. = 10, sig. = 0.000). In the factor
analysis, two factors were extracted explaining the 37.87 and 21.46% of the variance respectively.The
first factor was characterised by the variables GSE, M and CE and second one by PK and SEO.
Table A.1. Educational variables, items and reliability.
Learner factors. Reliability result (Cronbach’s Alpha) α = 0.579
GSE It’s easy for learners to persist to achieve their goals
α = 0.612 I am confident that learners’abilities can help them to effectively deal with any unexpected event (personal
or academic) during the term
Learners know how to manage their time to do well in this course
SEO Learners can learn from discussion in forum
α = 0.695 Students can learn in this online educational environment
I’m confident students can use technology to take part in this course
M This subject is relevant to learners’objectives
α = 0.357 Learners generally seemed motivated to do well in this course
Learners need additional motivation from instructor to complete their tasks
PK Learners should be able to apply knowledge obtained in other subjects in this subject
α = 0.656 Learners show strengths in some areas of the course
Learners count on prior knowledge for this course
CE The course information learners received before enrolling gave them an accurate picture of the course
α = 0.739 The expectations for the amount of coursework are fair
Learners will be able to keep up with the workload
Institutionalfactors.Reliabilityresult (Cronbach’sAlpha) = 0.857
LS Learners have received adequate training on the platform
α = 0.602 Learners had access to adequate tools and resources (library, modules, etc.) to learn in this course
Learners have received the technical support they needed when they had a problem
(Continued)

17. Open Learning 41
SP α = 0.738 Learners know that I am concerned about their needs as a learner
I have actively encouraged learners to participate in the course
I have developed a community sense among learners in this course
I I have used effective teaching strategies
α = 0.625 I have encouraged a variety of perspectives
I have a broad knowledge about my field
LP All important site content was easy to locate and identify
α = 0.673 The platform provided a clear means of obtaining technical help
The technological media used were appropriate for the content
II I returned all assignments with useful feedback
α = 0.816 I responded promptly to learners’questions
I provided individualised guidance that met learners’needs
LI Online comments by other participants helped students to learn
α = 0.792 Learners contributed to learning environment by responding their peers
Students learned to value other points of view
LC α = 0.779 Content was presented at an appropriate level for learners
Content was relevant to the objectives of the course
Content was stimulating for learners
CD The objectives of this course were evident in their learning activities
α = 0.691 The course material was presented in ways that suggested future application
Grades were directly related to learning objectives, activities and application of resources
Learningoutcomesfactors.Reliabilityresult (Cronbach’sAlpha) = 0.871
LST α = 0.804 Learners seemed motivated to do well in this course
Apart from the marks learners expected on this subject, this course was a useful learning experience
It is very likely that learners recommend other people to enrol in this online course
Students learned from the activities assigned in the course
The course was relevant to learners’needs
KA Learners did well on assignments and tests
α = 0.797 Learners can explain the content covered in this course to others
I have noticed the difference between learners’prior knowledge and the knowledge they have gained by
the end of the course
During the course, learners have been conscious about their strengths and weaknesses in their learning
Learners can make correct decisions and solve problems with the knowledge they have gained in this
course
AT Learners know how to use the course knowledge in new situations
α = 0.877 Learners have opportunities to apply the course knowledge
As a result of this course, learners are able to apply their learning to other similar courses
With the knowledge learners have gained from this course, they can more broadly explore a problem in the
field of study
As a result of this course, learners are able to apply their knowledge to a different context, such as their
personal or professional life
Table A.1. (Continued)

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