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Factors Predicting e-Learners' Satisfaction on Online Education

Conference Paper · August 2016

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Ayşenur Erdemir Vildan Bağcı

Gazi University Gazi University
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 Factors Predicting e-
Education

*Institute of Educational Sciences, Gazi University

erdemiraysenur@gmail.com, , vildanbagci@gmail.com,
e.cihat.corbaci@gmail.com

Abstract
The study aims to investigate the satisfaction of the learners who are taking part in the online
education and the factors affecting their satisfaction. The factors that are taken into
consideration are perceived usefulness, feeling of flexibility, feeling of security, active
participation, feeling free for asking questions, instructor response time, perceived ease of

essence of counselli -
Learning and a scale for perceived e-learner satisfaction were conducted. Students enrolled
in an online educational institution in Turkey are the subjects of the study. Data were
analysed using multiple regression analysis. The results revealed that students, regardless of
gender, who think that online education is useful and promotes active participation during
lessons as well as believe that it contributes their success are more satisfied on online
education. Support for study habits, feeling of flexibility and security are significant
predictors for male learners only. Also, flexibility and security of online education system

with
common significant predictors for girls.

Keywords: E-learning, learner satisfaction, online education

Main Conference Topic: Distance Education / E-Learning

Introduction
Online education has become prominent during recent years as the technology
develops. In online education, the information is delivered to the learners via
telecommunication technology. Internet has been so rapidly expanded and can be reached by
many people around the world. So, it is inevitable to use it as a course delivery platform.
Online learning is learning and communication via networked computers (online distance
education). In this respect, that online learning platforms are becoming important in teaching
and learning is an increasing trend (Volery & Lord, 2000).

There are some factors promoting online education. First one of them is economic
trend because lifelong learning becomes a competitive necessity. Higher education is also a
kind of need in order to have more chances of job. Secondly, academic trend in which
knowledge and information are growing exponentially is another important factor. There are

is technology trend. Technological devices are so common and more people from all
socioeconomic levels and age groups are using them more competently now (Cetron &
Daview, 2003). Based upon these factors, more courses, universities and additional
teaching/learning activities are becoming available through online education programs. Using
internet for distance education makes the interaction between teacher and learners possible.
 In Turkey internet and other technological devices are also used for educational
purposes. There are some university programs which have online courses and a few
institutions that have complementary courses for primary and secondary education. These
institutions are basically for the need of preparation for selection exams for next education
level. In Turkey there are secondary school/high school entrance exams for primary school
students and university entrance exams for secondary school students. In this study the focus
is on these complementary online courses provided by a private educational institution after
the routine school time so that the students can get extra preparation for these exams. Thus,
the study aims to investigate the satisfaction of the learners who are taking part in the online
education and the factors affecting their satisfaction. The factors that are taken into
consideration are perceived usefulness, feeling of flexibility, feeling of security, active
participation, feeling free for asking questions, instructor response time, perceived ease of
sfaction, belief in contribution to success, support for study habits, and
essence of counselling.

Related work
A different learning environment from the traditional classroom setting has been
created by online education and there are many thoughts related to its substance and
outcomes (Barber, Taylor & Buchanan, 2014). Means, Toyama, Murphy, Bakia and Jones
(2009) stated that online learners performed better than face to face learners. In addition, they
determined that the outcomes can be influenced by the content and teacher performance.
Storck and Sproull (1995) also showed that there is little or no difference in
performance between video instruction and face to face instruction. Webster and Hackley
(1997) suggested that effectiveness of these technology based systems need student
involvement and participation, cognitive engagement, technology self-efficacy (i.e., belief
that one is capable of interacting with a given technology), perceived usefulness of the
technology employed, and the relative advantage or disadvantage of online delivery.

Capability of using technology also influences the feeling of satisfaction of online

Web environment is the capability to engage by providing rapid, compelling interaction and
ortant element for teaching and learning and it
motivates learners. Teacher and learner characteristics also plays an important role in online
courses. The teacher should perform interactive teaching styles and s/he should encourage
interaction between the students and with the teacher. One of the most important learner
characteristics influencing online education is the gender of learners (Volery & Lord, 2000).
Being able to use computer easily is another variable which can have an interaction with
gender (Kay, 1992).

focus directly. Table 1 taken from Sun et al. (2007) has shown the related studies
summarized. Additionally, in their own study, a survey was conducted to investigate the
-learning. The results revealed that learner
computer anxiety, instructor attitude toward e-learning, e-learning course flexibility, e-
learning course quality, perceived usefulness, perceived ease of use, and diversity in

also stated that these results show institutions how to improve learner satisfaction and further
strengthen their e-learning implementation.
 Author(s) Factors
Arbaugh Perceived usefulness and perceived ease of use, flexibility of e-Learning,
(2000) interaction with class participants, student usage, and gender
Piccoli et al. Maturity, motivation, technology comfort, technology attitudes, computer
(2001) anxiety, and epistemic beliefs, technology control, technology attitudes, teaching
styles, self-efficacy, availability, objectivist and constructivist, quality,
reliability, and availability, pace, sequence, control, factual knowledge,
procedural, knowledge, conceptual knowledge, timing, frequency, and quality
Stokes (2001) san, and rational)
Arbaugh Perceived flexibility of the medium, perceived usefulness and perceived ease of
(2002) use, media variety, prior
Arbaugh and Perceived usefulness and perceived ease of use, perceived flexibility
Duray (2002)
Hong (2002) Gender, age, scholastic aptitude, learning style, and initial computer skills,
interaction with instructor, interaction with fellow students, course activities,
discussion sessions, and time spent on the course
Thurmond et Computer skills, courses taken, initial knowledge about e-Learning technology,
al. (2002) live from the main campus of the institution, age, receive comments in a timely
manner, offer various assessment methods, time to spend, scheduled discussions,
team work, acquaintance with the instructors
Kanuka and Motivating aims, cognitive modes, and interpersonal behaviors
Nocente
(2003)

Model
-item scale. Eleven factors
thought to be predictors of satisfaction were involved. The model is shown in Figure 1.

Figure 1: The model representation

Methodology
The Subjects and the Data
Küresel Online is an educational institution in Ankara/ Turkey catering online courses for the
primary school 2nd level and secondary school students in order to prepare them for the next
educational level entrance exams. They have students all around the Turkey. For this study,
the data were gathered from primary school 2nd level students (age of 11-14) of this
institution. For this level almost 1500 learners actively participate to the courses and other
activities provided by the online education system. The surveys were distributed via the
-learning learners attended the study. After

and the rest, 432, are girls. There were five items for the perceived e-learner satisfaction and
eleven items for the factors that predict the satisfaction of the learners.

Implementation
SPSS 22 was used to analyze data for this research. Firstly, for the satisfaction scale
exploratory factor analysis was conducted in order to prove its validity. The results show that
it has one dominant factor (KMO = 0,82; factor loadings: 0,47-0,72; total explained variance
= 62,92%). Factor scores are calculated for each learner showing their amount of satisfaction.
Then whether the data gathered from boys and girl are different or not was investigated. For
sampling distribution of mean difference, ANOVA was conducted in order to show whether
these two group are different. However, test of homogeneity of variance was significant,
which means the violation of an assumption of ANOVA. So, nonparametric version of it,
Mann-Whitney U, was conducted. The results show that the distribution of factor scores is
not the same across categories of gender. Therefore, subsequent analyses were done
separately for the girls and the boys. Lastly, multiple regression analysis with enter method
was used to prove the significance of the variables. Eleven predictor variables were applied
as independent variables, while perceived e-learner satisfaction was used as a dependent
variable. Assumptions for the regression analysis were summarized below.

Test of Assumptions
Variable types: All predictor variables must be quantitative and categorical (with two
categories) and the outcome variables must be quantitative, continuous and unbounded. In
this study, predictor variables have three categories and the outcome variable is
continuous.
Outliers: There should be no significant outliers, high leverage points or highly
influential points. In order to detect outliers, Mahalanobis and Cook distance are
2

used for multiple regression.

Non zero-variance: The predictors should have some variation in value. While range of
variance is between 0.130 and 0.755 for the data of boys, it is between 0.045 and 0.300
for girls. Therefore, it can be said that predictors have non zero variance.
No perfect multicollinearity: There should be no perfect relationship or high correlation
between two or more predictors. In order to test this assumption, VIF values are used. If
this value is between 1 and 10, there is no multicollinearity problem. VIF values for the
data of boys range between 1.826 and 3.264. VIF values for the data of girls range
 between 1.247 and 1.890. Therefore, it is concluded that there is no perfect relationship
which can mislead the results.
Homoscedasticity: At each level of predictor variables, variance of residual terms should
be constant. The studentized residuals against the unstandardized predicted values are
plotted and examined. For both data, points along the line of best fit remain similar as
moved along the line. This shows that variance of residual terms changes slightly.
Independent errors: For any two observations residual terms should be uncorrelated. This
assumption can be tested by using Durbin-Watson test. The test statistic can vary between
0 and 4, where 2 or close to 2 indicates adjacent residuals are uncorrelated. For both boys

uncorrelated/independent.
Normally distributed errors: It is assumed that residuals in the model are random and
normally distributed with a mean of 0. Normal Q-Q Plot of the studentized residuals were
examined for both data and it has been seen that residuals lay along the best fitting line.
Linearity: The mean values of the outcome variable for each increment of predictors lie
along a straight line. In order to test the assumption, partial regression plots are drawn.
For both data, predictors and the outcome variable have linear relationship.

Results
Multiple regression analyses were conducted for the data of girls and boys separately in
order to test if independent variables significantly predicted the dependent variable, namely
perceived e-learner satisfaction.

The results of the regression for boys indicated the predictors explained 70.5 % of the
variance (adjusted R2 = .695, F(11, 335) = 72.826, p<.01). Table 2 presents the results of
multiple regression analysis for boys.

Table 2. Results of multiple regression analysis for boys (n=347)

Predictors Standardized t value

Perceived usefulness .262 6.012**

Feeling of flexibility .093 2.168*
Feeling of security .105 2.020*
Active participation .111 2.606**
Feeling free for asking questions -.011 -.260
Instructor response time .018 .442
Perceived ease of use .065 1.221
.024 .445
Belief in contribution to success .257 4.843**
Support for study habits .122 2.384*
Essence of counselling -.013 -.327
Note: *p<.05; **p<.01.

When looked at the table, it can be seen that among eleven predictors, six are
significant with p value less than .05. These are perceived usefulness, feeling of flexibility,
feeling of security, active participation, belief in contribution to success and support for study
habits. Especially, it was found that belief in contribution to success significantly predicted e-

Furthermore, active participation, support for study habits, feeling of flexibility and security

compared to belief in contribution to success and perceived usefulness.

The results of the regression for girls illustrated the predictors explained 57.8 % of the
variance (adjusted R2 = .567, F(11, 420) = 52.294, p<.01). Results of multiple regression
analysis for girls are given in Table 3.

Table 3. Results of multiple regression analysis for girls (n=432)

Predictors Standardized t value

Perceived usefulness .079 2.070*

Feeling of flexibility .001 .030
Feeling of security .061 1.691
Active participation .190 4.599**
Feeling free for asking questions -.061 -1.488
Instructor response time .079 2.236*
Perceived ease of use .268 7.314**
.233 5.839**
Belief in contribution to success .199 4.571**
Support for study habits .083 1.940
Essence of counselling .038 1.045
Note: *p<.05; **p<.01.

When looked at the table, it can be said that among eleven predictors, six are significant
with p value less than .05. These are perceived usefulness, active participation, instructor
n contribution to

-learner satisfaction.
Furthermore, active participation and belief in contribution to success have also relatively

response time have significant but small affect compared to other predictors.

Conclusion
Multiple regression analysis was used to test which student characteristics significantly
predicted their satisfaction on online education. Analyses were carried out with respect to
gender. As expected, results differed from each other. When the results for boys and girls are
compared, there are three predictors which are significantly effect on e-learner satisfaction in
common. Those are perceived usefulness, active participation and belief in contribution to
success. In other words, students, regardless of gender, who think that online education is
useful and promotes active participation during lessons as well as believe that it contributes
their success are more satisfied on online education. Furthermore, variance in the outcome
explained by predictors in the data of boys are higher than explaine
education satisfaction.
 In addition to common significant predictors for boys and girls, support for study
habits, feeling of flexibility and security are significant predictors for male learners. Students
who think online education supports their study habits have higher satisfaction on online
education. Also, flexibility and security of online education system have positive effect on

for girls. It can be concluded that female students whose parents are satisfied with online
education are more satisfied with the system. Moreover, interface of online education system

For girls, getting response in time is very important for their satisfaction with online
education system.

References
[1] Cetron, M. J., & Davies, O. (2003). Special report: 50 trends shaping the future.
Bethesda, MD: World Future Society.
[2] Barber, W., Taylor, S & Buchanan, S. (2014). Empowering Knowledge-Building
Pedagogy in Online Environments: Creating Digital Moments to Transform Practice.
Electronic Journal of E-Learning. 12 (2): 128-137.
[3] Kay, R. (1992). An analysis of methods used to examine gender difference in
computerrelated behaviour. Journal of Educational Computing Research, No. 8, pp.
277-90.
[4] McIntyre, D.R. and Wolff, F.G. (1998). An experiment with WWW interactive learning
in university Education. Computers & Education, No. 31, pp. 255-64.
[5] Storck, J. and Sproull, L. (1995). Through A Glass Darkly ± What People Learn In
Videoconferences? Human Communication Research. No. 22. pp. 197-219.
[6] Sun, P. -C. et al. (2007). What drives a successful e-Learning? An empirical
investigation of the critical factors influencing learner satisfaction. Computers &
Education, doi:10.1016/j.compedu.2006.11.007
[7] Volery, T. & Lord, D. (2000) Critical success factors in online education. International
Journal of Educational Management, Vol. 14 Iss: 5, pp.216 - 223
[8] Webster, J. and Hackley, P. (1997). Teaching Effectiveness In Technology-Mediated
Distance Learning. Academy of Management Journal, Vol. 40 No. 6, pp. 1282-309
[9] McIntyre, D.R. and Wolff, F.G. (1998). An experiment with WWW interactive learning
in university Education. Computers & Education, No. 31, pp. 255-64.
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Brief biographies of the authors

Evaluation in Education, Gazi University. She has graduated from Middle East Technical
University with a degree of B.Sc. in Foreign Language Education and from Gazi University
with M.Sc. in Measurement and Evaluation in Education. As an English teacher, she was
She has been
working as a research assistant at the Institute of Educational Sciences, Department of
Measurement and Evaluation in Education, Gazi University, since 2013.

Derya Çavdar
Derya Çavdar is currently pursuing PhD at the Department of Measurement and
Evaluation in Education, Gazi University. She has graduated from Middle East Technical
University with a degree of B.Sc. in the Department of Elementary Mathematics Education
and from Gazi University with M.Sc. in Measurement and Evaluation in Education. As a
math teacher, she was employed for a public school in Turkey in between 2012 and 2013 and
then she was accepted as a Comenius assistant in Italy for one semester. She has been
working as a research assistant at the Institute of Educational Sciences, Department of
Measurement and Evaluation in Education, Gazi University, since 2013.

is currently pursuing PhD at the Department of Measurement and

Evaluation in Education, Gazi University. She has graduated from Gazi University with a
degree of B.Sc. in Elementary Mathematics Education and from Gazi University with M.Sc.
in Measurement and Evaluation in Education. She has been working as a research assistant at
the Institute of Educational Sciences, Department of Measurement and Evaluation in
Education, Gazi University, since 2013.

and
Evaluation in Education, Gazi University. He has graduated from Gazi University with a
degree of B.Sc. in Foreign Language Education and with M.Sc. in Measurement and
Evaluation in Education. As an English teacher, he was employed for a public school in

Institute of Educational Sciences, Department of Measurement and Evaluation in Education,

Gazi University, since 2013.