ArticlePDF Available

Designing a predictive model of student satisfaction in online learning

  • Hamdan Bin Mohammed Smart University

Abstract and Figures

Full Text available at link below (first come, ..) Higher education institutions consider student satisfaction to be one of the major elements in determining the quality of their programs. The objective of the study was to develop a model of student satisfaction to identify the influencers that emerged in online higher education settings. The study adopted a mixed method approach to identify issues perceived by students as affecting their satisfaction, using focus groups followed by exploratory and confirmatory factor analyses to develop the study model. Data were collected using an online questionnaire from a campus-wide sample of 834 students enrolled in a generic online course at the University of Mauritius. Using structural equation modeling, the study identified four significant determinants of student satisfaction in decreasing importance: the marketing construct of university reputation; physical facilities; faculty empathy; and student–student interactions. Various theoretical and managerial implications are discussed and directions for further research are proposed.
Content may be subject to copyright.
Full Terms & Conditions of access and use can be found at
Download by: [Heather Harvey] Date: 23 November 2015, At: 19:42
Journal of Marketing for Higher Education
ISSN: 0884-1241 (Print) 1540-7144 (Online) Journal homepage:
Designing a predictive model of student
satisfaction in online learning
Sanjai K Parahoo, Mohammad Issack Santally, Yousra Rajabalee & Heather
Lea Harvey
To cite this article: Sanjai K Parahoo, Mohammad Issack Santally, Yousra Rajabalee & Heather
Lea Harvey (2015): Designing a predictive model of student satisfaction in online learning,
Journal of Marketing for Higher Education, DOI: 10.1080/08841241.2015.1083511
To link to this article:
Published online: 21 Oct 2015.
Submit your article to this journal
Article views: 19
View related articles
View Crossmark data
Designing a predictive model of student satisfaction in online
Sanjai K Parahoo
, Mohammad Issack Santally
, Yousra Rajabalee
and Heather
Lea Harvey
HBMSU, Business School, Dubai, United Arab Emirates;
Centre for Innovative and Lifelong Learning,
University of Mauritius, Reduit, Mauritius;
Health Department, Thamassat University, Bangkok, Thailand
Higher education institutions consider student satisfaction to be
one of the major elements in determining the quality of their
programs. The objective of the study was to develop a model of
student satisfaction to identify the inuencers that emerged in
online higher education settings. The study adopted a mixed
method approach to identify issues perceived by students as
affecting their satisfaction, using focus groups followed by
exploratory and conrmatory factor analyses to develop the study
model. Data were collected using an online questionnaire from a
campus-wide sample of 834 students enrolled in a generic online
course at the University of Mauritius. Using structural equation
modeling, the study identied four signicant determinants of
student satisfaction in decreasing importance: the marketing
construct of university reputation; physical facilities; faculty
empathy; and studentstudent interactions. Various theoretical
and managerial implications are discussed and directions for
further research are proposed.
Received 7 February 2015
Accepted 20 May 2015
satisfaction; online learning;
reputation; interactions;
Higher education (HE) institutions consider student satisfaction to be one of the major
elements in determining the quality of their programs in todays markets (Yukselturk & Yil-
dirim, 2008), as student satisfaction is considered to be an important indicator of the
quality of academic experiences (Kuo, Walker, Belland, & Schroder, 2013; Yukselturk & Yil-
dirim, 2008). In this regard, the focus by universities on the marketing construct of student
orientation has been justied on academic grounds, since student satisfaction is related to
several desirable consequences for the students: quality of academic experiences (Allen &
Seaman, 2008; Gibson, 2010; Kuo et al., 2013), persistence (Allen & Seaman, 2008; Gibson,
2010), self-condence (Letcher & Neves, 2010), and retention (Debourgh, 1999).
From a business perspective, globalization and increasing competition among univer-
sities to attract students have led universities to adopt customer-oriented business models
in order to compete effectively (Newman & Jahdi, 2009; Parahoo, Harvey & Tamim, 2013).
Satised students are likely to engage in desirable behavior such as: spreading positive
© 2015 Taylor & Francis
CONTACT Sanjai K Parahoo
Downloaded by [Heather Harvey] at 19:42 23 November 2015
word-of-mouth, collaborating with the institution after they graduate (Alves & Raposo,
2009), and acting as potential donors as alumni (Parahoo et al., 2013). This makes these
satised students an asset to the university. As a result, the strategic focus of HE insti-
tutions has shifted from a teaching-oriented model to a customer-oriented model (Kuo
et al., 2013; Parahoo et al., 2013) so that universities may be considered as a provider of
products and services to their customers, namely students.
This shift in strategic orientation by universities is increasingly occurring within a new
paradigm of delivery of teaching and learning. There is a growing student request for
online courses, leading to online learning in higher education becoming a major mode
of delivery in todays technology-driven environment (Croxton, 2014; Platt, Raile, & Yu,
2014). In the USA, for example, demand for online courses has exceeded that for face-
to-face courses (Kuo et al., 2013). A similar trend is emerging in other markets, as
evidenced by the growing geographical distribution in the active membership of the Inter-
national Council of Distance Education, and the Commonwealth of Learning. The exibility
in participation, ease of access, and convenience offered by online learning to students
(Croxton, 2014), as well as a growing need for continuous and lifelong learning among
the working population, could be identied among the factors behind this surge in
Unsurprisingly, the global growth of HE has contributed to the development of a
rich student satisfaction literature. As a recognition of the emergence of online learning,
the empirical settings in recent studies have shifted from physical classes to online deliv-
ery. The issue of student satisfaction in online settings has emerged because the new tech-
nologies have altered the way that students interact with instructors and classmates
(Parahoo & Tamim, 2012; Yukselturk & Yildirim, 2008). This raises the question: Are the con-
siderations surrounding student satisfaction different between physical and online
In this regard, Platt et al. (2014) reported that students did not perceive online and
physical classes as being equivalent, as online courses were associated with fewer oppor-
tunities for interaction compared to face-to-face courses. McFarland and Hamilton (2005)
compared the determinants of satisfaction across traditional and online classes and found
that some determinants of satisfaction were specic to online learning: the effectiveness
of the discussion board and that of interactions with other students to support learning.
Other authors have focused on modeling student satisfaction in online learning using
multivariate linear regression to identify what antecedents of satisfaction emerged (e.g.
Beqiri, Chase, & Bishka, 2009; Kuo et al., 2013; Sher, 2009; Thurmond, Wambach,
Connors, & Frey, 2002). Given the variety of empirical settings and courses involved in
these studies, a number of antecedents emerged as affecting satisfaction, and these are
discussed in the next section. However, it is fair to state that consensus has yet to
emerge on a generic framework for predicting student satisfaction in online learning.
Further, the bulk of these studies have been undertaken in Western contexts and the nd-
ings may not be extensible to developing countries, which have different cultural, econ-
omic, and technological environments. It would therefore be pertinent to undertake
such studies in developing country environments.
The objectives of the study may therefore be stated as developing a model of student
satisfaction to identify the antecedent factors that emerge in online settings. More speci-
cally, the research objectives may be stated as:
Downloaded by [Heather Harvey] at 19:42 23 November 2015
(a) To identify the factors that inuence student satisfaction in online settings in a devel-
oping country.
(b) To develop and empirically validate a model of student satisfaction in such a setting.
(c) To identify the relative importance of the factors that inuence student satisfaction.
Given the dearth of empirical studies on student satisfaction in developing countries,
Mauritius was selected as an empirical setting for the study. Mauritius has a multi-
ethnic population of 1.3 million inhabitants comprising people of Indian, African,
Chinese, and French origins. Building on its infrastructure and a business-friendly
climate, the country successfully diversied its economy from agriculture and manufactur-
ing to a service economy based on tourism, nancial services, and technology. It has a
vibrant HE system with four public universities, including the University of Mauritius set
up in 1968, and the Open University of Mauritius which ofcially started its operations
in February 2013. The Tertiary Education Commission (TEC) is responsible for promoting
and managing the HE sector and the implementation of an overarching regulatory
quality assurance framework. As of December 2012, 67 private tertiary education insti-
tutions were registered with the TEC, many of which offered programs using distance edu-
cation or blended modes of delivery. The tertiary education sector employment is
estimated at 2700 persons, of which 30% are academic staff (Santally et al., 2013). The
gross tertiary enrollment rate, including Mauritian students enrolled in both local and
overseas HE institutions, as a percentage of the population aged 2024 years, stood at
46.6% in 2012 (Santally et al., 2013).
After decades of providing and rening online learning in Mauritius, this was the rst
research focused on developing a predictive model of student satisfaction in online learn-
ing. While generalizability of the ndings to other contexts should always be done with
circumspection, nonetheless it is hoped that the ndings will provide some broad direc-
tions to university administrators in developing countries, particularly those with compar-
able socio-economic contexts.
Literature review: student satisfaction and its predictors
Student satisfaction
Customer satisfaction occupies a central role in marketing. Desirable business imperatives
such as loyalty intentions (Alves & Raposo, 2009; Parahoo et al., 2013) have been found to
impact on the quality of academic experiences (Gibson, 2010; Kuo et al., 2013), leading to
student satisfaction (Letcher & Neves, 2010). Satisfaction thus constitutes the outcome vari-
able of the study model. Satisfaction has been dened as: A judgment that a product or
service feature, or the product or service itself, provides a pleasurable level of consump-
tion-related fulllment(Oliver, 1999, p. 34). Given the emergence of higher education as
a service experience, this denition is logically extendable to university contexts.
In a comprehensive review of the satisfaction literature in traditional face-to-face HE
settings, Gibson (2010) reviewed major studies and classied the antecedents of
student satisfaction across nine factors: academic staff/teaching; classes/curriculum; advis-
ing support; skills developed by students; preparation for future; services/facilities; social
integration; student centeredness/responsiveness; and pre-enrollment factors.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
During the past decade, various scholars have investigated student satisfaction in an
online setting and they identied interactivity as one of its key predictors. Kuo et al.
(2013) surveyed 102 HE students in the USA, and using regression analysis, determined
that the interactions of students with instructors, with the course materials, as well as inter-
net self-efcacy, determined their satisfaction. Similarly, Sher (2009) surveyed HE students
in Washington, DC, and using linear regression, found that interactions among students
and between students and faculty were the two factors affecting student satisfaction.
Endres, Chowdhury, Frye, and Hurtubis (2009) reported similar ndings. Endres et al.
(2009) surveyed 277 students in an online MBA program in the Midwest (USA) and
using discriminant analysis, found that student satisfaction was determined by ve
factors: satisfaction with faculty practices, learning practices, course materials, student-
to-student interaction, and course tools. Bollinger and Martindale (2004) conducted a
study involving 105 graduate students enrolled in multiple online courses in instructional
technology at a regional university in the USA to identify factors inuencing student sat-
isfaction. Using factor analysis, they unearthed three components affecting student satis-
faction: the instructor; technology; and interactivity. Finally, in professional online courses,
the design of the course work, assessments, and providing timely feedback on assign-
ments were found to affect student satisfaction (Thurmond et al., 2002).
Other authors have focused on the demographic proles and personal attributes that
would help to predict student satisfaction in an online HE context. For example, Beqiri et al.
(2009) surveyed 240 business students in the USA and they determined that male and
married postgraduates were more likely to be satised, and with regression analysis, it
was further found that factors such as the appropriateness of and student familiarity
with the course also inuenced their satisfaction.
With the growth of technology-enabled learning environments, online learning platforms
now enable considerable scope for synchronous interactions among students as well as
between the students and the instructor. Alternatively, in asynchronous mode, threaded
discussions are often used to support interactive discussions and exchange of ideas
among students or between students and the instructor. It has been proposed that
online courses with high levels of interactivity result in higher levels of student motivation,
improved learning and satisfaction, as compared to less interactive learning environments
(Croxton, 2014).
Moore (1989) examined the issue of interactions and proposed a framework compris-
ing three types of interactions that are important in online education: studentstudent,
studentteacher, and studentcontent. Studentstudent interaction refers to two-way
reciprocal communication among learners who exchange information, knowledge,
thoughts, or ideas regarding the course (Moore, 1989). Studentteacher interaction con-
sists of two-way communications between the teacher and students, while student
content interaction is a process of individual students elaborating and reecting on
the subject matter or the course content (Moore, 1989). Numerous recent studies
have shown that these interactions play a critical role in the student academic experi-
ence and student satisfaction (Endres et al., 2009;Kuoetal.,2013;Parahoo&Tamim,
2012;Sher,2009). Although interactions are recognized as critical in online education,
Downloaded by [Heather Harvey] at 19:42 23 November 2015
the literature is inconclusive regarding which type(s) of interaction would be more
important in predicting student satisfaction (Bray, Aoki, & Dlugosh, 2008).
In support of the impact of studentstudent interactions on satisfaction, Ivankova and
Stick (2007) surveyed 207 doctoral students and determined that those who successfully
completed their studies received more meaningful and constructive peer feedback than
those who dropped out. Similarly, Einarson and Matier (2005) used multiple linear
regression and found that students who had a sense of belonging and benetted from
social engagement were more likely to be satised with their educational experience.
Finally, in a qualitative study of marketing students, Hollenbeck, Mason, and Song
(2011) found that studentstudent interactions reduced the perceived threat of poor per-
formance in their courses.
While studentstudent interactivity plays an important role in online student satisfac-
tion, it has been proposed that quality and timeliness of studentteacher communication
is also a major predictor of student satisfaction (Croxton, 2014), supported by various
empirical studies. Such examples include Walker and Kelly (2007) where online under-
graduate and graduate students reported that the timeliness of instructor feedback inu-
enced overall course satisfaction. Parahoo et al. (2013) found similar results in Saudi Arabia,
using multiple regression analysis showing that various elements of faculty interactions
(including empathy, availability of faculty, and promptness of faculty feedback) had a sig-
nicant impact on satisfaction for males.
The interactions between studentstudent and studentteacher in online learning
have been investigated more commonly than studentcontent interaction (Kuo et al.,
2013), and have been found to be more predictive of student satisfaction than
learner-content interaction (Bolliger & Martindale, 2004). On this basis, the focus in this
study was therefore on studentstudent and studentteacher interactions. Support for
such interactions is found in the service-dominant logic literature, which argues for
the involvement of customers in co-creating value in a service. In essence, facilitating
effective interactions of students among themselves and with faculty would support
higher involvement of the students in the teaching and learning process.
University reputation
An often cited denition of corporate reputation is the ‘“observerscollective judgments of a
corporation based on assessments of the nancial, social, and environmental impacts attrib-
uted to the corporation over time(Barnett, Jermier, & Lafferty, 2006, p. 34). Achieving a posi-
tive reputation is crucial to a business as it represents the most valuable intangible asset
(Vidaver-Cohen, 2007). The importance of reputation to organizations seems to have
extended to universities, as supported by the recent research ndings (Gibson, 2010; Helge-
sen & Nesset, 2007; Parahoo et al., 2013; Sung & Yang, 2009).
While Gibson (2010) found that only two studies identied reputation as a determinant of
satisfaction, other recent studies have established the relevance of reputation in satisfaction
models. For example, in a study in Dubai, Parahoo and Tamim (2012) surveyed 99 students
enrolled in an online program and found that university reputation signicantly affected
student satisfaction. Kuo and Ye (2009) used structural equation modeling (SEM) and found
a positive relationship between university image and student satisfaction at a Taiwanese
vocational institute.In Norway, Helgesen and Nesset (2007) used SEM to establish a signicant
Downloaded by [Heather Harvey] at 19:42 23 November 2015
relationship betweenreputation and loyalty, for online undergraduates. Similar ndings relat-
ing to the university reputation-student satisfactionrelationship have been reported by Sung
and Yang (2009) and Parahoo et al. (2013).
Assessing reputation from student perceptions presents a difculty. Students are in
essence being required to assess their own university post-purchase emotions. Cognitive
dissonance theory would suggest that students at a lower ranked university would not
be comfortable in reporting their university reputation as second rate, so they would
tend to over-rate the reputation of their university to compensate (Lake Wobegon
effect). To work around this dilemma, this study identied a range of diverse items
that would collectively and comprehensively constitute the perceived reputation of a
It is recognized that an organizations reputation has been built over time by con-
sistently meeting its stated objectives. Consequently, the studentsperception of the
reputation of a HE institution would be used by the students as a proxy to reduce
the risk of having an unsatisfactory academic experience. This would be particularly
relevant in online learning environments where students interact at a distance with
the organization, and hence would rely on more intangible cues to make satisfaction
judgments. In this regard, the lack of research emphasis on reputation in the HE litera-
ture, particularly in an online context, may be a bit surprising, and it would be useful to
investigate the construct of reputation and its role in satisfaction models for online
Physical facilities
A construct that complements reputation is the quality of the physical facilities of the uni-
versity. The facilities dimension is broadly referred to as tangibles, physical features, and
physical issues by various researchers (Farahmandian, Minavand, & Afshardost, 2013). It
is associated with accessibility of physical facilities by students to support academic and
nonacademic activities. In their seminal work on services, Parasuraman, Zeithaml, and
Berry (1988) identied tangiblesas one of the ve dimensions of service quality, a con-
struct closely associated with satisfaction.
Subsequent research has supported this proposition. For example, Gibson (2010)
surveyed the student satisfaction literature and found strong evidence for the effect of
physical facilities on satisfaction. Similarly, Helgesen and Nesset (2007) established a sig-
nicant relationship between facilitiesand satisfaction, and Arambewela and Hall
(2009) found a positive association between physical infrastructure and satisfaction
among international postgraduate business students in Australia. Elsewhere, Farahman-
dian et al. (2013) surveyed postgraduate students in Malaysia and established that facilities
signicantly affected student satisfaction, as did Parahoo et al. (2013) in a survey of stu-
dents in Saudi Arabia, where a factor (labeled reputation) which included items relating
to physical infrastructure had a signicant effect on satisfaction both for male and
female students.
Research evidence thus suggests that the academic experience of students extends
beyond human interactions to other aspects that affect student life, such as physical
characteristics of university facilities (Parahoo et al., 2013). Although the facilities-satisfac-
tion linkage has been supported by the literature on service quality (e.g. Parasuraman
Downloaded by [Heather Harvey] at 19:42 23 November 2015
et al., 1988), other researchers have not identied such a relationship. For example, ODris-
coll (2012) used factor analysis and multiple regression analysis in a study of 263 under-
graduate hospitality students in Ireland and found that physical facilities did not have a
signicant impact on satisfaction.
Of particular relevance is the fact that most of the evidence pertains to traditional phys-
ical settings. Therefore, it would be pertinent to examine to what extent online setting
impacts on the importance of physical facilities in inuencing student satisfaction. Does
the fact that the teaching and learning now takes place in a virtual context inuence
the perceived importance of facilities by students?
In conclusion, it would be useful to examine and quantify the impact the variables of
interactions, reputation, and physical facilities have on student satisfaction in an online
setting by designing a model of satisfaction and testing it using SEM.
Empirical setting
In line with the objectives of the study, a generic course offered by the University of
Mauritius General Education module (GEM) was selected as the sampling frame for a
number of reasons. First, the GEM had an enrollment of over 800 undergraduate stu-
dents from the different schools on campus, thereby representing a university-wide
sample. Walker and Kelly (2007)identied behavioral differences in interactions
between undergraduate and postgraduate students. Therefore, focusing on UG stu-
dents in the present study enabled one to control the possible confounding effect of
level of study. Further, the course was offered fully online, thereby leading the students
to be fully immersed in the online learning experience and hence to be primed to
report on their satisfaction. An online survey was administered for convenience and
anonymity at the end of the online course. The students were assured of the conden-
tiality of their responses, and that any analysis would be done on an aggregate basis
without identifying individual respondents. This approach worked well, and at the
end of the survey period, a total of 834 usable responses were received, representing
a response rate of 90%.
Mixed method: combining qualitative and quantitative approaches
The study used a mixed methodapproach to achieve its objectives. Such an approach
has been recognized by Johnson, Onwuegbuzie, and Turner (2007) as the third major
research approach, as it combines the benets of both qualitative and quantitative
methodologies. In the present exploratory study, this approach enabled identifying com-
prehensive issues affecting student satisfaction, developing a conceptual model, and
then validating the model empirically.
The rst stage involved a qualitative phase using two focus groups, each containing
eight undergraduate students. This enabled detailed information to be obtained about
individual and group feelings, perceptions, and opinions as well as seeking clarica-
tions about the ideas expressed by the students. The discussions were digitally
recorded for ease of transcription. The discussions were then analyzed in depth inde-
pendently by each author to ensure reliability and minimize researcher bias. A
Downloaded by [Heather Harvey] at 19:42 23 November 2015
meeting was then held between the researchers to resolve discrepancies and reach
agreement regarding the emerging themes affecting student satisfaction and hence
to complete the coding process. The major themes affecting satisfaction that
emerged were:
.Quality of studentsinteractions with faculty;
.Quality of studentsinteractions with other students;
.Quality of studentsinteractions with IT staff;
.Quality of studentsinteractions with administrative staff;
.Faculty prole in terms of academic competence, teaching experience, and compe-
tence in using technological tools;
.Quality of physical facilities of the university;
.Corporate reputation of the university.
Development of conceptual model and hypotheses
This qualitative phase was followed by a quantitative one involving exploratory and
conrmatory factor analyses to determine and conrm the factor structure of
student satisfaction. Six variables inuencing satisfaction emerged: IT/Administrative
staff interactions;Faculty empathy;Reputation of university;Student interactions;Phys-
ical facilities;and Faculty feedback.
Four of these factors (IT/Administrative staff interactions;Faculty empathy;Student inter-
actions;and Faculty feedback) related to different types of interactions that students were
involved in. Based on Moore (1989), studentstudent interactions represented one of the
three types of interactions that led to satisfaction, and this has been supported by
various empirical studies discussed in the literature review section (e.g. Einarson &
Matier, 2005; Endres et al., 2009; Hollenbeck et al., 2011; Ivankova & Stick, 2007; Sher,
2009). Therefore, the rst hypothesis is:
H1: Studentstudent interactions have a positive inuence on student satisfaction
Similarly, the wide empirical support for studentteacher interactions (represented in the
present study by two distinct factors faculty empathy and faculty feedback) constituted
major antecedents of student satisfaction (see literature review section for discussion of
supporting studies by Croxton, 2014; Thurmond et al., 2002; Walker & Kelly, 2007;
Parahoo et al., 2013). Consequently, it is proposed:
H2: Faculty empathy has a positive inuence on student satisfaction
H3: Faculty feedback has a positive inuence on student satisfaction
The online setting supported the emergence of a related type of interaction that was not
strictly part of Moores(1989) framework. Studentadministrative/IT staff interactions
emerged as a determinant of student satisfaction during the qualitative phase and
was conrmed in the exploratory factor analysis (EFA) and conrmatory factor analysis
(CFA). This factor seems to be subsumed into the facilities/servicesclassication of
Gibson (2010) which was identied in other studies, among them LeBlanc and
Nguyen (1997), Thomas and Galambos (2004)andParahooandTamim(2012)provided
further support for the relationship between student interactions with IT/administrative
Downloaded by [Heather Harvey] at 19:42 23 November 2015
staff and student satisfaction, when they used multiple regression and empirically
determined a signicant relationship between the two constructs. Thus the fourth
hypothesis is:
H4: Studentadministrative/IT interactions have a positive inuence on student satisfaction
A positive association between reputation of universities and student satisfaction was
demonstrated by various researchers (Kuo & Ye, 2009; Parahoo et al., 2013; Parahoo &
Tamim, 2012; Sung & Yang, 2009). Therefore:
H5: University reputation has a positive inuence on student satisfaction
Helgesen and Nesset (2007) used SEM and demonstrated that university facilities exerted a
signicant effect on student satisfaction. Similar ndings were determined by LeBlanc and
Nguyen, (1997) and Thomas and Galambos, (2004), while Parasuraman et al. (1988) estab-
lished a positive relationship between facilities and service quality (a key antecedent of
satisfaction). It is therefore proposed that:
H6: Physical facilities at a university have a positive inuence on student satisfaction
These relationships may be summarized as per Figure 1.
Figure 1. Conceptual model.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
Developing pool of items
For each variable identied, validated measures were sourced from the literature and ne
tuned to the context of the present study. For example, measures pertaining to the differ-
ent types of interactions students had with other stakeholders, as well as their perceptions
of the universitys physical facilities, were sourced from SERVPERF (Cronin & Taylor, 1992)
and HEdPERF (Firdaus, 2005). For university reputation, items from Sultan and Wong (2012)
and Parahoo et al. (2013) were selected, while for satisfaction, common satisfaction scales
(e.g. Butcher, Sparks, & OCallaghan, 2001; Sivadas & Baker-Prewitt, 2000) were used. At the
conclusion of this process, a battery of 34 items deemed to inuence student satisfaction
with their university was drafted. These items were pilot tested with a sample of 10 stu-
dents, which resulted in additional renement, yielding the draft study questionnaire,
ready for item purication.
Item purication and nalization of measurement scales
The data collected were cleaned and coded, and the analysis comprised of four stages.
First, an EFA was conducted using SPSS 20 to identify the number of distinct factors
involved (Hair, Black, Babin, & Anderson, 2010). The emerging factor structure was then
conrmed by CFA using LISREL. Items having loadings of less than 0.5 on their respective
factors were considered for omission, thereby achieving purication of scales (Hair et al.,
The internal consistency of the different study scales was measured through composite
reliability (CR) using a value of 0.70 as a threshold (Hair et al., 2010). Face, convergent, and
discriminant validity were also assessed. Face validity was incorporated into the measures
by using existing validated measures. Convergent and discriminant validities were
assessed by using average variances extracted (AVE) statistics and squared correlations
between study variables. Finally, the study hypotheses were tested by structural equation
modeling (SEM) using LISREL. Full details of these statistical techniques are described in
the next section.
Exploratory factor analysis
A sample size of 834 respondents exceeded the minimum size required for EFA as per Hair
et al. (2010). The assumptions underlying EFA were conrmed with the KaiserMeyer
Olkin measure of sampling adequacy being 0.930, while the Bartletts test of sphericity
had a p-value < .001. The EFA was conducted using principal component analysis for
extraction followed by varimax rotation. Seven factors involving 29 items emerged with
eigenvalues exceeding 1.0, which cumulatively accounted for 59.1% of the variance (see
Table 1). The factors were named as follows: Student satisfaction;StudentIT/Administrative
staff interactions;Faculty empathy;Reputation of university; Studentstudent interactions;
Physical facilities;and Faculty feedback.
Since the loading and variance for satisfaction were substantially larger than those of
other factors, the possibility of common method bias (CMB) was tested, as its presence
would threaten the validity of the conclusion drawn upon statistical results (Podsakoff,
Mackenzie, & Podsakoff, 2012). Harmans test was undertaken with the number of
Downloaded by [Heather Harvey] at 19:42 23 November 2015
factors to be extracted constrained to one, and assessing the value of percentage of var-
iance explained by the rst component. The results showed a variance of 31.9%, well
below the threshold of 50%, thereby conrming the absence of CMB in the data.
Scale construction: unidimensionality
The 29 scale items representing 7 measurement scales were tested for the unidimension-
ality of each individual scale using conrmatory factor analysis (Hair et al., 2010). The t
indices for the overall models were satisfactory (chi-square/degrees of freedom ratio =
2.90). On analyzing the measurement models, it was observed that while all the paths
were statistically signicant (p< .01), and the vast majority of 29 paths (lambdas) had
Table 1. Rotated factor solution.
Item description (simplied)
Student satisfaction
Very satised with university services .692
University met my expectations .808
University fullled my aspirations .803
University met my needs .751
StudentIT/administrative staff interactions
IT Staff are accessible .698
Administrative staff provide dependable information .693
IT staff provide good technical support .817
IT staff provide good technical training .769
Administrative staff provide timely support .524
Faculty empathy
Faculty are caring .702
Faculty respond promptly to requests for assistance .690
Faculty are interested to solve my problems .724
Faculty display a positive attitude towards me .701
Reputation of university
Faculty are competent .458
Faculty have long teaching experience
University is known for excellent quality programs .517
University is known for its reputable programs .593
University has a good reputation in market .513
Faculty use technology effectively
Faculty communicate using electronic tools
Studentstudent interactions
There is good collaboration among students during
There is good communication among students for
group assignments
There is good collaboration among students during the
Physical facilities
University campus layout is attractive .778
University facilities are visually appealing .750
University has a comfortable physical environment .732
Faculty feedback
Faculty provides timely feedback .781
Faculty provides detailed feedback .798
Faculty are available for consultation
Eigen value 10.5 2.1 1.8 1.6 1.2 1.1 1.1
Cumulative percentage of variance (%) 31.9 38.2 43.8 48.5 52.2 55.7 59.1
Items with loadings < 0.50 on their construct (subsequently deleted after CFA).
Downloaded by [Heather Harvey] at 19:42 23 November 2015
loadings above the desirable value of 0.70, four of the path loadings had values below the
minimum prescribed threshold of 0.50 (Hair et al., 2010). Three of these paths pertained to
reputation (relating to faculty experience, faculty using technology effectively and faculty
communicating electronically) and one pertained to faculty feedback (available for consul-
tation). After consultations among the authors and with colleagues from the Admissions
Department, it was considered that these four items could be deleted without affecting
the conceptualization of the related constructs. Consequently, these four items were
omitted and the CFA was run again with 25 measures. As expected, the chi-square/
degrees of freedom ratio (= 2.50) and the t indices improved: SRMR = 0.050; RMSEA =
0.032; NNFI = 0.98; CFI = 0.99.
Examining reliability and validity
As illustrated in Table 2, the CR of each of the seven scales was good (> 0.70 as
recommended by Hair et al., 2010). Face validity was established by using validated
measures from the literature. Convergent validity was established using the criteria
of Hair et al. (2010) by examining the path loadings, which were all high. Further,
the AVE at least equaled 0.5 for all constructs (see Table 2), thereby conrming con-
vergent validities. Discriminant validities between a pair of latent variables were estab-
lished by comparing the two respective AVE values to the square of the respective
correlation estimates between the same two variables. Since the AVE values exceeded
the squared correlation estimates, discriminant validity was established.
Having established unidimensionality, reliability and validity for each of the measure-
ment scales, it was appropriate to investigate the study hypotheses.
Prole of respondents
All the undergraduate students surveyed were relatively young, with 72.5% being below
20 years, and 99% below 25. Most of the students were female (62.4%) as compared to
male (37.6%), and the students were primarily single (98.1%).
Testing of hypotheses
The conceptual model shown in Figure 1 above was tested by SEM. It was observed that all
paths pertaining to the 25 items (see Table 1) in the measurement models were signicant
at p< .01, while in the structural model, all paths were similarly signicant (p< .01) except
Table 2. Summary statistics for scale items (AVE values on diagonal, squared correlation estimates
below diagonal).
Item description #Items CR 1234567
1 Student satisfaction 4 0.92 0.74
2 StudentIT/admin Interactions 5 0.84 0.26 0.52
3 Faculty empathy 4 0.80 0.30 0.34 0.50
4 Reputation of university 4 0.80 0.48 0.31 0.31 0.50
5 Studentstudent interactions 3 0.84 0.15 0.14 0.13 0.15 0.64
6 Physical facilities 3 0.81 0.44 0.24 0.22 0.40 0.44 0.59
7 Faculty feedback 2 0.73 0.16 0.22 0.30 0.04 0.04 0.11 0.58
Downloaded by [Heather Harvey] at 19:42 23 November 2015
for two paths: administrative/IT interactionsstudent satisfaction (gamma = 0.01, t=
0.19) and faculty feedbackstudent satisfaction (gamma = 0.03, t= 0.70). Consequently,
these two non-signicant paths were omitted and the revised model containing four inde-
pendent variables reected by 18 items was again tested by SEM.
The model t was good, and supported by a chi-square/degrees of freedom ratio = 2.60,
with the following t indices: SRMR = 0.035; RMSEA = 0.044; NNFI = 0.99; CFI = 0.99, all well
within the threshold proposed by Hair et al. (2010). Consequently, hypotheses H3 and H4
were rejected, while H1, H2, H5, and H6 could not be rejected (Figure 2).
Figure 2. Final structural and measurement models (p< .01 for all model paths).
Downloaded by [Heather Harvey] at 19:42 23 November 2015
Theoretical implications
The study used EFA to identify six factors that inuenced student satisfaction in online set-
tings in Mauritius: StudentIT/Administrative staff interactions; Faculty empathy; Repu-
tation of university; Studentstudent interactions; Physical facilities; and Faculty
feedback. These factors conrm those identied from three preceding studies that used
a similar methodology (summarized in Table 4 of Parahoo et al., 2013). However, there
is one notable addition: Faculty empathy.
Empathy had a signicant effect on satisfaction, and relates to the following attributes
displayed by the faculty: caring approach and positive attitude towards students, prompt-
ness in responding to studentsqueries, and interest to help them (see Table 1). While it
would be anticipated that students would expect some empathy from their faculty in an
educational setting, it seems that the physical separation of students from their peers and
instructors in the online course, in Mauritius, magnied studentsexpectations of empathy,
allowing it to emerge as a separate factor. The reasons for this expectation may be inves-
tigated further using qualitative methodologies.
The modeling of student satisfaction presented additional interesting ndings. Four dis-
tinct variables signicantly affected satisfaction (p< .01). In decreasing order of impor-
tance, they were identied as: Reputation of university (gamma = 0.47); Physical facilities
(gamma = 0.27); Faculty empathy (gamma = 0.17), and Studentstudent interactions
(gamma = 0.06).
The key role of reputation is noteworthy, particularly since Gibson (2010) in his literature
review found that reputation/image was identied in only two student satisfaction studies.
Therefore, this study highlights the emerging role of university corporate reputation in
affecting student satisfaction and conrms similar ndings (e.g. Kuo & Ye, 2009;
Parahoo et al., 2013; Parahoo & Tamim, 2012; and Sung & Yang, 2009). It is interesting
to note that in addition to university and program quality/image, the reputation construct
comprised one item relating to perceived faculty competence (see Table 1). This highlights
the fact that in online education, the contribution of faculty in delivering teaching and
learning service retains an important role, unaffected by technology and the availability
of digitized content.
Physical facilities had a signicant effect on satisfaction in an online context, thereby
supporting similar ndings determined previously (e.g. LeBlanc & Nguyen, 1997;
Thomas & Galambos, 2004). The relevance of physical facilities (dened by esthetic
design and layout and comfortable facilities, see Table 1) is interesting, as it demonstrates
that despite the online setting, physical facilities retain their importance in the minds of
students. This might imply that in an intangible service, the students used quality of phys-
ical facilities as a proxy for quality of academic experience. In this regard, the precise role of
facilities in online learning contexts as compared to brick and mortar universities should
be further investigated.
Finally, the present study found that studentstudent interactions had a signicant
effect on satisfaction. This nding supports those of preceding researchers who estab-
lished similar ndings (e.g. Einarson & Matier, 2005; Hollenbeck et al., 2011; Ivankova &
Stick, 2007). The comparatively lower effect of this type of interaction on satisfaction
Downloaded by [Heather Harvey] at 19:42 23 November 2015
may be due to the online nature of the course which limits opportunities for interaction
among students. Further, studentstudent interaction and studentinstructor interaction
were empirically determined to be more predictive of student satisfaction than
studentcontent interaction in most studies of online learning (Kuo et al., 2013). It
would therefore seem that students attached more importance to interactions with
their faculty members and with fellow students as compared to provided course
content. Understanding why studentcontent interactions did not emerge as a factor
inuencing satisfaction brings about further questions as to whether such a situation
emerges due to open educational resources available freely on the internet, so that stu-
dents look more for human interactions (with faculty and fellow students) to share experi-
ences, discuss and comprehend the course concepts.
In line with the research objectives, the study developed and empirically validated a model
of student satisfaction in an online setting in a developing country. It was determined that
university reputation was the most important factor to achieve student satisfaction, fol-
lowed by physical facilities, faculty empathy, and studentstudent interactions. Based
on these ndings, university management may develop a marketing strategy to
enhance student satisfaction in its online courses. Student satisfaction has desirable con-
sequences both from an academic perspective, including student persistence, self-con-
dence, performance, retention and matriculation rates, as well as from the business
perspective of student loyalty, positive word of mouth, support from alumni and future
Limitations and further research
While a large sample size, survey of the whole course population, and the scientic meth-
odology used give increased condence in the ndings, the results nonetheless need to
be treated with caution. The limitations include the fact that it was undertaken among
undergraduate students in a single university in a developing country, and may not be
generalizable to other contexts. Therefore, extension to other regional and global contexts
would need to be undertaken with utmost care due to differences in socio-cultural
environments. For this reason, it would be useful to replicate the study in other universities
to establish generalizability in a broader context. The key roles of reputation and physical
facilities on student satisfaction also warrant more in-depth investigation, as well as how
these issues t in solely online learning institutions. Finally, since the present study focused
on undergraduate students, it would also be useful to determine whether postgraduate
students demonstrate similar determinants of their satisfaction.
Managerial implications
It would make sense for administrators of the university to recognize the key role that
reputation plays and hence invest resources in developing desirable images projected
by their university in the higher education market, thereby beneting from desirable con-
sequences. Since the reputation construct included an item related to the perceived
Downloaded by [Heather Harvey] at 19:42 23 November 2015
competence of faculty, the administrators need to invest in faculty recruitment, develop-
ment and retention.
The physical facilities of the university retained an important effect on student satisfac-
tion, despite the online context of the course. Therefore, university administrators should
ensure both esthetic and comfortable physical environments. While this may seem para-
doxical, particularly for online learning, it seems that the physical facilities represent a reas-
surance to students in terms of being a proxy for quality academic experiences.
Furthermore, the ndings showed that in the online setting particularly, a need for the
faculty to demonstrate empathy towards their students was critical for student satisfaction
and took on several forms, including adopting a caring approach and positive attitude
towards the students, promptness in responding to studentsqueries and exhibiting a
genuine interest to help. This requires that online faculty demonstrate a different set of
social traits as compared to those in traditional settings. It may require a special effort
for faculty to adapt to this new social requirement brought by the online course
context. Therefore, faculty who teach online courses may need to discuss with their
course coordinators the perceived need for empathy by online students, so that faculty
expectations may be reconciled with those of students, and a common ground be deter-
mined that is mutually benecial.
While the impact of studentstudent interactions on their satisfaction is well documen-
ted (e.g. Moore, 1989), and university administrators need to ensure there are ample
opportunities for such interactions, in the current study, such interactions had by far
the lowest impact on satisfaction. This is somewhat surprising, as quality of peer
support and interactions have been established to have a strong effect on student satis-
faction in online learning. These ndings would tend to point to a gap in opportunities for
student interactions, so that university management may consider setting up active online
forums, blogs and wikis, designing part of the formative assessments around group work,
and stimulating physical interactions to break the ice, particularly at university orientation
and during other events.
The study provides management with a predictive model of student satisfaction which
supports various academic benets (quality of learning, self-condence, retention and
matriculation rates). The established consequences of satisfaction in terms of student
loyalty are equally important, as satised students spread positive word of mouth and con-
stitute the foundation of a strong relationship with their university. As alumni, they
support the establishment of strong industry linkages (for placements, collaborative
research, and curriculum review), and may further support the university through future
On a concluding note, it seems that the recent focus by universities on student orien-
tation by viewing themselves as providers of a quality service in order to attract prospec-
tive students(Kuo & Ye, 2009) has altered the dynamics of university-student relationships
and expectations. This ts with the strategy of developing a strong corporate reputation
that has been well established in the business satisfaction literature (Vidaver-Cohen, 2007).
The corollary of this change in student expectations is that students now frame their
behavior within a business service perspective, probably as they would for any other com-
mercial service. In line with the contentious debate about customer orientation in acade-
mia, universities need to give serious thought to reconciling academic requirements (e.g.
expecting learners to invest time and effort required in developing knowledge, skills, and
Downloaded by [Heather Harvey] at 19:42 23 November 2015
competencies) and service imperatives (based on a logic of minimum customer efforts in
enjoying a service). Striking this right balance is crucial to universities for maintaining aca-
demic excellence while meeting service imperatives.
The authors would like to thank the two anonymous reviewers and the editor for their insightful
comments which helped us to improve the article.
Disclosure statement
No potential conict of interest was reported by the author(s).
Allen, I. E., & Seaman, J. (2008). Staying the course: Online education in the United States, 2008, The
Sloan consortium. Babson Survey Research Group, 23 p. Retrieved from http://www.sloanc.
Alves, H., & Raposo, M. (2009). The measurement of the construct satisfaction in higher education.
The Service Industries Journal,29(2), 203218.
Arambewela, R., & Hall, J. (2009). An empirical model of international student satisfaction. Asia Pacic
Journal of Marketing and Logistics,21(4), 555569.
Barnett, M. L., Jermier, J. M., & Lafferty, B. A. (2006). Corporate reputation: The denitional landscape.
Corporate Reputation Review,9(1), 2638.
Beqiri, M. S., Chase, N. M., & Bishka, A. (2009). Online course delivery: An empirical investigation of
factors affecting student satisfaction. Journal of Education for Business,85(2), 95100.
Bolliger, D. U., & Martindale, T. (2004). Key factors for determining student satisfaction in online
courses. International Journal on E-Learning,3(1), 6167.
Bray, E., Aoki, K., & Dlugosh, L. (2008). Predictors of learning satisfaction in Japanese online distance
learners. International Review of Research in Open & Distance Learning,9(3), 124.
Butcher, K., Sparks, B., & OCallaghan, F. (2001). Evaluative and relational inuences on service loyalty.
International Journal of Service Industry Management,12(4), 310327.
Cronin, J. J. Jr., & Taylor, S. A. (1992). Measuring service quality: A re-examination and extension.
Journal of Marketing,56(3), 3355.
Croxton, R. A. (2014). The role of interactivity in student satisfaction and persistence in online learn-
ing. MERLOT Journal of Online Learning and Teaching,10(2), 314325.
Debourgh, G. (1999). Technology is the tool, teaching is the task: Student satisfaction in distance learn-
ing. Paper presented at the Society for Information and Technology & Teacher Education
International Conference, San Antonio, TX.
Einarson, M. K., & Matier, M. W. (2005). Exploring race differences in correlates of seniorssatisfaction
with undergraduate education. Research in Higher Education,46(6), 641676.
Endres, M. L., Chowdhury, S., Frye, C., & Hurtubis, C. A. (2009). The multifaceted nature of online MBA
student satisfaction and impacts on behavioral intentions. Journal of Education for Business,84(5),
Farahmandian, S., Minavand, H., & Afshardost, M. (2013). Perceived service quality and student sat-
isfaction in higher education. IOSR Journal of Business and Management,12(4), 6574.
Firdaus, A. (2005). The development of HEdPERF: A new measuring instrument of service quality for
the higher education sector. International Journal of Consumer Studies,30, 569581.
Gibson, A. (2010). Measuring business student satisfaction: A review and summary of the major pre-
dictors. Journal of Higher Education Policy and Management,32(3), 251259.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis. Englewood Cliffs,
NJ: Prentice Hall.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
Helgesen, O., & Nesset, E. (2007). Images, satisfaction and antecedents: Drivers of student loyalty? A
case study of a Norwegian university college. Corporate Reputation Review,10(1), 3859.
Hollenbeck, C. R., Mason, C. H., & Song, J. H. (2011). Enhancing student learning in marketing courses:
An exploration of fundamental principles for website platforms. Journal of Marketing Education,33
(2), 171182.
Ivankova, N. V., & Stick, S. L. (2007). Studentspersistence in a distributed doctoral program in edu-
cational leadership in higher education: A mixed methods study. Research in Higher Education,48
(1), 93135.
Johnson, R. B., Onwuegbuzie, A. J., & Turner, L. A. (2007). Toward a denition of mixed methods
research. Journal of Mixed Methods Research,1(2), 112133.
Kuo, Y. C., Walker, A. E., Belland, B. R., & Schroder, K. E. (2013). A predictive study of student satisfac-
tion in online education programs. The International Review of Research in Open and Distributed
Learning,14(1), 1639.
Kuo, Y. K., & Ye, K. D. (2009). The causal relationship between service quality, corporate image and
adultslearning satisfaction and loyalty: A study of professional training programmes in a
Taiwanese vocational institute. Total Quality Management,20(7), 749762.
LeBlanc, G., & Nguyen, N. (1997). Searching for excellence in business education: An exploratory
study of customer impressions of service quality. International Journal of Educational
Management,11(2), 7279.
Letcher, D. W., & Neves, J. S. (2010). Determinants of undergraduate business student satisfaction.
Research in Higher Education Journal,6(1), 126.
McFarland, D., & Hamilton, D. (2005). Factors affecting student performance and satisfaction: Online
versus traditional course delivery. Journal of Computer Information Systems,46(2), 2532.
Moore, M. G. (1989). Three types of transaction. In M. G. Moore & G. C. Clark (Eds.), Readings in prin-
ciples of distance education (pp. 100105). University Park: The Pennsylvania State University.
Newman, S., & Jahdi, K. (2009). Marketisation of education: Marketing, rhetoric and reality. Journal of
Further and Higher Education,33(1), 111.
ODriscoll, F. (2012). What matters most: An exploratory multivariate study of satisfaction among rst
year hotel/hospitality management students. Quality Assurance in Education,20(3), 237258.
Oliver, R. L. (1999). Whence consumer loyalty? Journal of Marketing,63(Special Issue), 3344.
Parahoo, S. K., Harvey, H. L., & Tamim, R. M. (2013). Factors inuencing student satisfaction in univer-
sities in the Gulf region: Does gender of students matter? Journal of Marketing for Higher Education,
23(2), 135154.
Parahoo, S. K., & Tamim, R. M. (2012). Determinants of student satisfaction in higher education: An
empirical study in Dubai. International Journal of Services, Economics and Management,4(4),
Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Servqual. Journal of retailing,64(1), 1240.
Platt, C. A., Raile, A. N., & Yu, N. (2014). Virtually the same? Student perceptions of the equivalence of
online classes to face-to-face classes. Journal of Online Learning & Teaching,10(3), 489503.
Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science
research and recommendations on how to control it. Annual Review of Psychology,63, 539569.
Santally, M., Sungkur, R., Fagoonee, I., Halkhoree, R., Swarts, & P. Mikkonen, J. (2013). Assessment of
environmental, institutional and individual leadership capacity needs for the knowledge society in
Mauritius. Global e-Schools and Communities Initiative, (pp. 1021). Retrieved from http://www.
Sher, A. (2009). Assessing the relationship of student-instructor and student-student interaction to
student learning and satisfaction in web-based online learning environment. Journal of
Interactive Online Learning,8(2), 102120.
Sivadas, E., & Baker-Prewitt, J. L. (2000). An examination of the relationship between service quality,
customer satisfaction, and customer loyalty. International Journal of Retail and Distribution
Management,28(2), 7382.
Sultan, P., & Wong, H. Y. (2012). Service quality in a higher education context: An integrated model.
Asia Pacic Journal of Marketing and Logistics,24(5), 755784.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
Sung, M., & Yang, S. U. (2009). Studentuniversity relationships and reputation: A study of the links
between key factors fostering studentssupportive behavioral intentions towards their university.
Higher Education,57(6), 787811.
Thomas, E. H., & Galambos, N. (2004). What satises students? Mining student-opinion data with
regression and decision-tree analysis. Research in Higher Education,45(3), 251269.
Thurmond, V. A., Wambach, K., Connors, H. R., & Frey, B. B. (2002). Evaluation of student satisfaction:
Determining the impact of a web-based environment by controlling for student characteristics.
The American Journal of Distance Education,16(3), 169190.
Vidaver-Cohen, D. (2007). Reputation beyond the rankings: A conceptual framework for business
school research. Corporate Reputation Review,10(4), 278304.
Walker, C. E., & Kelly, E. (2007). Online instruction: Student satisfaction, kudos, and pet peeves.
Quarterly Review of Distance Education,8(4), 309319.
Yukselturk, E., & Yildirim, Z. (2008). Investigation of interaction, online support, course structure and
exibility as the contributing factors to studentssatisfaction in an online certicate program.
Educational Technology & Society,11(4), 5165.
Downloaded by [Heather Harvey] at 19:42 23 November 2015
... Even though online teaching and learning have become increasingly popular in recent years, student satisfaction with online learning experiences continues to be one of the most important indicators of the overall quality of online teaching and learning experiences (Ilgaz and Gulbahar, 2015). Furthermore, for higher education institutions, student satisfaction with online learning experiences continues to be one of the most critical factors in determining the quality of their online teaching and learning (Ilgaz and Gulbahar, 2015;Parahoo, Santally, Rajabalee & Harvey, 2016). Student satisfaction with the learning experience can have an impact on a variety of online interactions, including student-student, student-instructor, and student-content interactions. ...
Full-text available
With the concepts of online teaching, the study examines online teaching in light of the COVID 19 pandemic effects. Online teaching is a new and different alternative to traditional teaching forced upon most governments and education systems in several developing countries due to the pandemic, which raised several questions for teachers, students, and their parents. To this end, the study's primary goal was to learn about the perception of teachers as well as students and parents regarding the satisfaction, merit, and challenges of online teaching. Teachers, students, and parents were all polled in this study. The findings of this study revealed that there is a difference in satisfaction with online teaching, as well as the perceptions of teachers, parents, and students, depending on gender. Along with the merits and challenges of online teaching the current study also examines the perceptions of teachers regarding the difficulties associated with online learning, which in major cases was related to the technology.
... Online learning with high social interaction levels between students and instructor have been suggested to offer greater motivation, better learning and satisfaction for students (Croxton, 2014). While, interactions between students minimised the possible risks of bad result in their classes (Parahoo et al., 2016). ...
The implications of online learning versus face-to-face learning have been discussed for several years in higher education. Hence, this study’s objective was to assess the learning satisfaction towards Online Distance Learning (ODL) and its relationship with ODL readiness in the context of Physical Education setting. This quantitative research adopted an online survey method that measure the ODL readiness and learning satisfaction among 172 Physical and Health Education students who are practicing ODL due to Corona Virus Disease (COVID-19) pandemic. The results indicate that ODL readiness is more likely to affect the learning satisfaction among students while having ODL session and it led to low satisfaction level in learning. The results of learning satisfaction level showed a small effect in accordance to gender; male students are anticipated to have slightly higher than female students due to dissimilarity of skill possessed. On this basis, it is recommended the ODL readiness among students should be considered when performing ODL classes in order to achieve high learning satisfaction towards ODL.
... On the other hand, Parahoo et al. (2016) built a predictive model about the satisfaction of the students in online higher education considering the marketing construct of university reputation, physical facilities, faculty empathy and interactions. ...
Full-text available
SARS-CoV-2 virus has caused universities to update their courses in the distance modality. The general aim of this mixed research was to build and analyse the use of a web application for the educational process about the t-test considering data science. In particular, the professor of the Teaching of Mathematics II course needed to update the school activities because of the new educational demands caused by the COVID-19 pandemic. To facilitate the educational process of math, this teacher decided to build a web application that presents the formulas and calculation of the mean, standard deviation and statistical error to understand the use of the t-test. This technological tool allows the personalisation of learning through the simulation of data. The participants were 42 students from a Mexican university. The results of machine learning indicated that the contents of the web application positively influenced the assimilation of knowledge, satisfaction during the learning process, development of mathematical skills and learning in the distance modality. The decision tree technique allows the construction of four (4) predictive models about the use of the web application for the educational process about the t-test. Finally, educators have the opportunity to improve the teaching-learning conditions during the SARS-CoV-2 virus through the design and construction of web applications.
... The Kuo et al. (2013), Parahoo et al. (2016), Sher (2009. Specifically, a better interaction amongst students increases their satisfaction in e-learning at the start of the COVID-19 pandemic. ...
Full-text available
The end of the COVID-19 pandemic that directly impacts students’ learning cannot be predicted with certainty. Previously dominated by face-to-face learning methods, student learning has fully transitioned into full e-learning, or online/distance learning provides a completely new experience for students. Students are important learning recipients and university stakeholders. Therefore, much attention should be paid to their learning satisfaction to ensure that higher education’s learning process is conducted well during a pandemic. The absence of quantitative empirical research on the drivers of e-learning satisfaction in the setting of private higher education is the theoretical impetus for this study. This study evaluated a learning satisfaction model during (early) the COVID-19 pandemic. An online questionnaire survey with a sample of 722 undergraduate students from a top-ranking private university was conducted in Indonesia, which reported the highest number of COVID-19 cases in Southeast Asia in 2020. Survey results identify the social presence, confirmation, and student-student interaction as the drivers of e-learning satisfaction during the pandemic. Moreover, robust learning system quality has a significant indirect influence on learning satisfaction that is mediated by student-student interaction. The findings of this study can provide implications for private university administrators in Indonesia to pay attention to and make improvements related to social presence, confirmation, learning system quality, and student-student interaction during a pandemic.
Full-text available
Full-text available
The increase in online education creates a need to explore how learning outcomes, student satisfaction, and student perceptions about online courses are affected by prior online learning experiences. This study examined the role of prior online learning experience on students’ perceived cognitive presence, social presence, teaching presence, engagement, and satisfaction. The archival data of online learners at a large midwestern university (a total n=878), including survey responses related to Community of Inquiry (CoI), engagement, and satisfaction, were utilized to conduct statistical analyses to determine whether student responses differed by the number of online courses taken previously. We found that only social presence scores (CoI sub-scale) and emotional engagement scores (engagement sub-scale) differed by the number of the online courses taken. However, the effect size was small. We concluded that student satisfaction, engagement, and perceptions of cognitive and teaching presence are not related to prior online course experiences. Implications are discussed.
Full-text available
Full issue of Vol. 26 No. 4
Full-text available
The pandemic caused major disruptions in academic life and led educational institutions to adopt online learning which is likely to leave its mark on post-pandemic higher education. The aim of this study was to contribute to the effort of overcoming the challenges of higher education during the fragile period of transitioning to the post-pandemic era. The objectives were to investigate undergraduate students’ experience during and after the pandemic and to identify the factors that affect their satisfaction with online and in-person learning. To meet these objectives, environmental students, recruited with multistage sampling, were administered questionnaires. Results showed that satisfaction with in-person learning was higher than online learning pointing to a preference for face-to-face modes of education. Although students were optimistic during the transition to the post-pandemic period, the pandemic caused students more stress over their studies than economic difficulties. Moreover, students’ satisfaction with online learning was mostly affected by their anxiety about their studies due to the pandemic, their demographic characteristics, and the type of information sources they used to obtain information about COVID-19. On the other hand, satisfaction with in-person learning was affected by information sources on COVID-19 and their parents’ occupation. Finally, students acknowledged the importance of protecting the environment and biodiversity in order to prevent pandemic outbreaks in the future.
Conference Paper
Full-text available
Due to the pandemic, the whole world has experienced a distance education experience. Turkey has not been left out of this experience. According to some educational scientists, distance education experienced in this process should be expressed in terms of distance instruction, distance teaching, e-learning, online education, and emergency remote education. Aside from how it will be named, the experience has been experienced in different field educations without adapting to the educational requirements and characteristics of the field (such as medicine, engineering, educational sciences, law, social science education). This research focused on the distance education satisfaction levels of medical school students. In addition, the level of satisfaction according to gender, year repetition and year of education was also compared within the scope of the research. 678 students studying in different years of Çanakkale Onsekiz Mart University Faculty of Medicine participated in this survey type study. Analyzes were performed with JAMOVI statistical software and nonparametric analyses. According to the results of the research, the satisfaction level of the medical faculty students from distance education is moderate. When the satisfaction levels of distance education are examined by gender, the satisfaction levels of male students are higher than female students. There was no significant difference between the satisfaction levels of students from distance education according to their year repetition. It was determined that 1st and 3rd years students were more satisfied with distance education than 2nd, 4th and 5th years students. For this reason, it is recommended to repeat this study in different fields (such as engineering education, educational sciences education), different universities and different groups.
Full-text available
An active learning group work course at “A” university in Japan is characterized by two-way interactions between students as well as between teachers and students. The spread of COVID-19 prompted a shift from in-person lessons to online synchronous lessons in 2020 and 2021. This mixed methods study analyzes data from a combined structured and open-ended questionnaire completed by 5,268 students. The results showed that online lessons were significantly more highly evaluated than face-to-face lessons in terms of enhancing students' understanding of student life, sense of belonging, expressing one's opinions and listening to those of others, and self-regulation of attendance and gaining an in-depth understanding of the course material. However, face-to-face lessons were preferred for small class sizes, interactions with students who have different ideas, and group learning activities. Open-ended responses indicated that conducting online classes via Zoom improved students' perceptions of group learning and interaction in this setting.
Full-text available
Japanese distance education has been slow to utilize the Internet, and mainly depends on the mail system and, to a lesser extent, television broadcasting as its mode of delivery. Since 2001, however, regulations have been relaxed to allow students to complete all course requirements for a university degree via online distance learning. This paper reports the results of a questionnaire study administered to the students (N = 424) enrolled in one of Japan's few online distance universities. Satisfaction with learning was explored by examining students' opinions and learning preferences in regard to five aspects of distance learning identified as important: (1) learner-teacher interaction, (2) learner-content interaction, (3) learner-learner interaction, (4) learner-interface interaction, and (5) student autonomy. In addition, the analysis included students' responses to three open-ended questions. Results indicate that students were generally satisfied with their learning, and that, specifically, learning satisfaction was higher for students who: (1) could persevere in the face of distance learning challenges, (2) found computers easy to use, (3) found it easy to interact with instructors, and (4) did not prefer social interaction with others when learning.
Full-text available
While various research studies have focused on antecedents and consequences of student satisfaction, few studies have done so in the Gulf region. The objective of the present study was therefore to design and empirically examine a model of student satisfaction in a private university in the Gulf region that operates in a high-technology-enabled environment. Based on a literature review and conducted focus groups, draft measures for the study constructs were developed. Data were collected from 217 students and an exploratory factor analysis identified 6 factors that potentially influenced satisfaction. After scale development, multiple regression analysis was used to test the research questions. It was found that the two genders displayed a difference in the factors influencing their satisfaction. For female students, only reputation (beta ¼ .499, p , .01) was significant, while for male students, both reputation (beta ¼ .763, p , .01) and perceived faculty academic competence (beta ¼ .301, p , .01) were significant. Various theoretical and managerial implications are discussed.
In higher education, students are the main customers of universities. As such, providing quality services and satisfying students' needs as well as expectations are vital for universities to succeed from the increasing competitiveness of this industry. This research investigates the levels of student satisfaction and the relationship between student satisfaction and the quality of service being provided at the International Business School, UniversitiTeknologi Malaysia Kuala Lumpur. The results of this research indicated that almost the majority of students were satisfied with the quality of services offered at this university. Also, the findings showed that, the factors of facilities, advisory services, curriculum, and financial assistance and tuition costs have positive and significant impact on student satisfaction.
Interest in online course delivery has increased in recent years, and a body of research has emerged regarding this trend. Many of the studies compare student performance online versus in a traditional class (and find none), or differences in student satisfaction (and find online students to be generally less satisfied than their traditional counterparts). The purpose of this study is threefold: (1) to see if careful control between online and traditional sections can alleviate the generally lower satisfaction of online students, (2) to preliminarily propose a set of factors that could lead to increased performance and satisfaction for online students, and (3) to confirm previous work, which indicates that student performance online is no different than performance in a traditional classroom. The study involved senior-level undergraduate MIS students who were enrolled in an E-Business course; results suggest several possible ways to increase student performance and satisfaction in online courses.
The purpose of this study was to investigate key factors influencing student satisfaction with online courses. The Biner instrument (1993) was modified to accommodate questions relating to online courses. One-hundred five respondents out of a sample of 303 online learners completed the resulting Online Course Satisfaction Survey. The results indicated student satisfaction with online courses is influenced by 3 constructs: instructor variables, technical issues, and interactivity. Results indicated the instrument is a valid measure of student satisfaction with online courses
We develop and empirically validate a student satisfaction modelling technology-enabled university environments. We use focus groups at a university in Dubai and an intensive literature review to propose a theoretical model that involves different types of student interactions; perceptions of infrastructure; and university branding as independent variables influencing student satisfaction as outcome variable. Using data collected from a random sample of 99 students, we empirically test the model using linear regression analysis. Two variables, branding and interactions of students with administrative staff are found to significantly influence student satisfaction, accounting for 61% of variance. Implications are discussed and suggestions for future research are given. With its specific context, the study requires replication in other countries to determine whether the findings are generalisable. This study is one of the rare occasions when a structural model of student satisfaction in technology-enabled environments in the Middle East has been subjected to empirical scrutiny.