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Abstract

in this paper we seek to understand the outcomes of online education by observing the role of learning styles. Under the assumption that the behaviors that maximize learning are dependent on the delivery method, we compared learning outcomes of students participating in two classes set up on an interactive e-learning platform. Our results show that learning styles are variables worthy of consideration in online settings, even though the correlation among learning styles and test results does not indicate a significant association. Specifically, we argue that online education demands a particular set of behavioral patterns (i.e., low companionship, achievement orientation) necessary to navigate the eccentricity of online education (e.g., social isolation, schedule flexibility). We discuss the theoretical implications of our results in the context of online education and offer practical suggestions for online teaching design.
VARK Learning Styles and Online Education: Case Study
Ermira Idrizi
Faculty of Computer Science and
Engineering,
Ss. Cyril and Methodius University
Skopje, R. Macedonia
ermiraidrizi@gmail.com
Sonja Filiposka
Faculty of Computer Science and
Engineering,
Ss. Cyril and Methodius University
Skopje, R. Macedonia
sonja.filiposka@finki.edu.mk
Vladimir Trajkovik
Faculty of Computer Science and
Engineering,
Ss. Cyril and Methodius University
Skopje, R. Macedonia
trvlado@finki.edu.mk
Abstractin this paper we seek to understand the outcomes of
online education by observing the role of learning styles. Under the
assumption that the behaviors that maximize learning are
dependent on the delivery method, we compared learning
outcomes of students participating in two classes set up on an
interactive e-learning platform. Our results show that learning
styles are variables worthy of consideration in online settings, even
though the correlation among learning styles and test results does
not indicate a significant association. Specifically, we argue that
online education demands a particular set of behavioral patterns
(i.e., low companionship, achievement orientation) necessary to
navigate the eccentricity of online education (e.g., social isolation,
schedule flexibility). We discuss the theoretical implications of our
results in the context of online education and offer practical
suggestions for online teaching design.
KeywordsQuality of Experience; VARK; Online Education.
I. INTRODUCTION
Online education is commonly considered a form of distance
education because students are physically separated from each
other and the instructor. This teaching approach features
electronic learning or e-learning, which relies on computer
network technology, often via the Internet, to transfer
information from instructors to participants and vice versa and
is a widely spread teaching approach in higher education
institutions.
Significant research has followed the increasing academic
interest in online education, with particular attention to
understanding the efficiency of the online teaching approach
compared to classroom teaching [1][2]. The student experience
in online classes is a different one from traditional face-to-face
classes, and patterns of engagement seem to differ between the
two. For example, in online classes students felt more detached
from their peers and professors, more compelled to be self-
sufficient in their studies, and less assisted by their professor,
than their professors believe them to be. Students can also feel
overwhelmed by the technological assumption of online study,
particularly if they start off without enough technical knowledge
or support. Researchers have recommended that unlike the
faster, real-time speed of face-to-face classes, the additional time
available for online activities might allow students to think about
the course material more critically and reflectively, leading to
deeper understanding of the course content. Others have
suggested that the less challenging or personal nature of e-
learning might give confidence to shyer students to engage
more, or to feel less pressure than in face-to-face interactions.
The perceived usefulness and the user’s attitude are used to
predict the intention of the students to use the system. The
relationship between Quality of Service (QoS), Quality of
Experience (QoE) and online learning tools, have been
investigated in [3][4]. Deeper understanding of factors
influencing QoE in higher education should be investigated in
order to create better utilization of the resources in the distance
learning environment [5][6]
A better understanding of how student’s learning styles
affect their academic performance during online classes would
lead to better adapting, designing and evaluating online classes
and so the students grasp of QoE would increase leading to
increased satisfaction with online education. The focus of this
paper is to determine how learning styles affect student’s
academic performance, and how this differs when comparing
traditional classes with online classes. The method of
investigating how learning styles influence the quality of
achieved learning results using different media presentations
and delivery styles is presented. We performed a quality of
experience (QoE) study including 70 students from the Faculty
of Computer Science and Engineering that enrolled two distance
learning courses from the computer science study program, both
set up on the faculty’s Moodle interactive e-learning platform.
In the study we introduce the subjective Learning Style (VARK)
variable. The results analysis shows how the subjective learning
style correlate to the delivery method offered for following the
online course.
II. CHARACTERISTICS OF ONLINE EDUCATION
Online education offers a variety of advantages for students
and education institutions, while changing the scheme of
education. Flexible schedules seem to be one of the most
appealing attributes of online education. The broad accessibility
to technology enables online students to do class work anytime
anywhere.
Consequently, the measure of learning in online courses
heavily relies on the students, who can choose convenient times
to concentrate on learning. This feature has proven to be of great
value, especially to students facing irregular schedules. Other
advantages ascribed to online learning include reduced travel
time and expenses [7].
Online learning mostly consists of blended learning and fully
online courses. Blended learning primarily employs face-to-face
sessions, including distance learning/lecturing sessions, and
online materials that are also provided to students. Fully online
learning has no face-to-face sessions, and most learning
processes are provided through an online environment.
Therefore, this type of instruction can present students with
freedom from learning restrictions.
The new concept for online education known as Massive
Open Online Courses (MOOCs) is available for both blended
and fully online courses and is attracting the interest of both
educators and students. Though institutes of higher education
recognize some potential benefits, the impact on teaching and
learning is still being discussed. On the other hand, it has often
been suggested that a great deal of these participants have
difficulty with continuing their education online, leading to
aggravating drop-out rates [8]. While the world-wide use of
MOOCs as fully online courses has increased rapidly, the course
completion rate is still one of the most serious problems
impacting their success.
Based on these features of online learning in addition to the
quality of the online program, personality and learning styles
play a tremendous aspect on student’s academic performance
since online learning methods differ from traditional classrooms.
III. LEARNING STYLES VARK
Learning is a complex process of achieving knowledge or
skills involving a learner's biological characteristics/senses
(physiological dimension); personality characteristics such as
attention, emotion, motivation, (affective dimension); cognitive
dimension; and psychological/individual differences
(psychological dimension). In this paper we analyze the
physiological dimension of learning styles focusing on what
senses are used [9]. The popular typology for the physiological
dimension of the learning styles is VARK (Visual, Aural,
Read/Write, and Kinesthetic):
Visual: visual learners like to be provided with
demonstrations and can learn through descriptions. They
are distracted by movement or action but not by noise.
Aural: aural learners learn by listening. They like to be
provided with aural instructions and they appreciate aural
discussions. They are easily distracted by noise.
Read/write: read/write learners take notes. They often
draw things to remember them. They do well with hands-
on projects or tasks.
Kinesthetic: kinesthetic learners learn best by doing.
Their preference is for hands-on experiences. They
prefer not to watch or listen and generally do not do well
in the classroom.
We assume that the set of learning styles are differently
apportioned in an online course than in a face-to-face course.
Typically, online learning systems include less sound or oral
segments than traditional face-to-face courses and these online
learning systems have more capacity of read/write assignment
components [10]. Students with visual learning styles and
read/write learning styles may do better in online courses than
their complement in face-to-face courses [11].
IV. METHODOLOGY
The main goal of this paper is to determine if learning styles
are influential in the participation of students who are enrolled
in online or more traditional courses [12]. This study used a field
experiment to empirically test our belief that different types of
education materials delivery combined with learning styles and
character traits affect student’s academic performance [13].
A. Participants
For this study the sample populations were students enrolled
in two courses Search Engine (C1) and Dynamic Websites (C2)
at the Faculty of Computer Science and Engineering, Ss. Cyril
and Methodius University in Skopje, R. Macedonia. The two
distance learning courses “Search Engines” and “Designing
Dynamic Web Sites” were set up on the faculty’s MOODLE
interactive e-learning platform.
Students were informed about the experiment before the
beginning of the course. A few of them decided not to participate
in this experiment, some discarded the experiment. Thus, the
total number of participants which took part in this experiment
was 70 (40 females and 30 males)..The mean age of participants
was 21 ranging from 19 to 22. Participants were divided
randomly into two groups of 35 students (group A and B). For
the duration of one semester the selected students attended both
courses. Their learning results were tested at the final exam. For
motivating students to participate seriously they gained an extra
credit for their course grade based on their individual
performance. This study tried to determine how character traits
and learning styles affect student’s academic performance
especially while taking online classes.
B. Course delivery
As introduced earlier, for the purpose of this research we
have created two experimental courses (C1 and C2) with two
groups of students (group A and group B). The first course (C1
course) can be considered as slightly less advanced course on
introduction to computer science, while the second (C2 course)
is a more advanced course that requires some previous
knowledge in computer science. The participants were randomly
chosen from students enrolled on both courses. In order to
experiment with the character traits and preferred student
learning styles, we used different presentation types, for
delivering the educational content of each course:
Offline document content - PDF documents,
presentations and url links with related content were
designed and spread to students. This makes it possible
for students to independently manage their time and learn
at their own selected pace.
Offline video content - video presentations were
recorded and delivered to the students in the form of a
streaming video. This gives the opportunity for students
to watch the material presented in a more animated
fashion but still create their own learning schedule.
Online video conferencing - live video conferences were
prepared with the professor of each course. The lectures
were scheduled at fixed time, and students needed to be
enrolled for the appropriate course in order to be able to
participate in the video conference. This delivery method
requires that students attend classes at fixed times, so it
differs from the previous delivery methods were students
had the freedom of organizing their time at their own.
But, at the end of the lecture, students have the
opportunity to cooperate with the professor and among
themselves.
For each course, students were divided into two groups (A
and B). The A group of students that attended the C1 course,
were asked to choose their preferred content delivery type (one
of the three educational contents described above). According to
their choice, they were divided into three stereotypes, and to
each stereotype the lectures were presented according to their
preference, the other group of students that attended the C1
course, had no chance to choose the preferred content delivery
type. The choice of the type of education materials delivery (one
of the three types described above) was made by the professor,
without taking into deliberation the student’s preferences.
For the C2 course, students from B group choose their
preferred content delivery type; while students from A group
were given the content delivery type choose by the professor. At
the end of both courses (C1 and C2), a survey was carried out
with the participants in order to gather feedback results about the
students’ observation for the quality of experience during those
two courses.
C. Procedures
This experiment was conducted at the Faculty of Computer
Science and Engineering, Ss. Cyril and Methodius University in
Skopje. Two significant groups of students (A and B), each
containing three subgroups, as described in the previous section
were organized for comparison purposes. The Moodle
interactive interface was used for management of the student-
content during the experiment, as well as for the teacher-content
interaction. None of the students had accessed the material
previous of the experiment.
All participants attended both courses during one semester.
At the beginning of the semester the participants received a short
explanation about the way the experiment will be carried out
together with their required duties. During the experiment
students were asked to complete questionnaires: personality
questionnaire (character traits), questionnaires about their
preferred learning styles, questionnaire indicating their
intentions to continuously use various educational content
delivery types as well as questionnaire for assessing the
students’ QoE. The experiment was completed with a final
exam, through which students’ learning outcomes were tested.
For the purposes of this study we processed and analyzed the
data collected from the final exam, their preferred learning styles
and their character traits, and also the data from QoE survey. We
compared the final test results in terms of correlation coefficient
of test results and character traits, correlation coefficient of test
results and learning styles for groups A and B. We also analyzed
the character traits and learning styles preference influences on
the test outcomes for both groups of students.
V. RESULT ANALYSIS AND DISCUSSION
Learning outcome relates to the degree of knowledge
gathered by a person after studying certain material. We
analyzed how learning styles affect the learning results during
those courses and their exam scores of the two groups of students
on both courses, after study sessions. And at the end we analyzed
the Quality of Experience students experienced during this
experiment.
A. Correlation Coefficient for Learning styles
The VARK questionnaire was used to determine learning
styles of students who participated in two online courses. The
VARK instrument positions each student against the four
distinct learning styles: visual (V); aural (A); reading/writing (R)
and kinesthetic (K). These four ranges are used to analyze the
suitability of online learning structures.
Fig. 1. Average VARK indicators against the students preferred learning
method C1
In Fig. 1 and Fig. 2, the averaged results from the VARK
questionnaire are presented in groups based on the students
preferred learning method for both courses C1 and C2. On the x
axis are shown the preferred way of materials delivered, and on
the y axis are shown the average of each learning style based on
the preferred way of materials delivered on the scale from 0 to
10 which corresponds to the questionnaire where 0 represents no
relation between the learning style and the way of delivered
materials, and 10 represents a strong relation of the given
learning style and the preferred content delivery type.
As expected, the more visual and aural inclined students
prefer video streaming or video conference, while the reading
oriented are more inclined on using PDF materials. It is
interesting to note that many of the students consider themselves
as kinesthetic learners across all offered learning methods.
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Fig. 2. Average VARK indicators against the students preferred learning
method - C2
We have compared the two sample groups, based on the
correlation coefficient of how test results and learning styles
correlate. To find the correlation coefficient we used the proper
excel function, which returns the correlation coefficient of the
arrays of cell ranges. The equation for the correlation coefficient
is:
𝐶𝑜𝑟𝑟𝑒𝑙 𝑋, 𝑌 = * Σ(x − x)(y − y)
Σ(𝑥 − 𝑥)2Σ(𝑦 − 𝑦)2
where x and y are the sample means AVERAGE (array1)
and AVERAGE (array2).
Based on this formula we have examined the correlation
coefficient on how learning styles are related with the test results
taken by the students enrolled on the courses, see Fig. 3. For the
first course Group A of students who had the opportunity to
choose the preferred way of delivered materials the correlation
coefficient is in the higher positive range (V=0.5; A= 0.29; R=
0.14; K=0.19) than that for the second sample Group B
(V=0.24; A=-0.17; R= 0.25; K=0.53).
It is worth noting that for the first course Search Engineseven
though for the first group the correlation coefficient has a more
positive trend still the difference varies a lot for the different
learning styles, as we can observe Kinesthetic has the highest
correlation coefficient for the second group probably based on
what type of learning materials students were handed out and
how they interacted with them, while students see this learning
style as a favorite since they desire to make more hand on tasks
rather than only listening in classes.
Fig. 3.Correlation coefficient of two sample groups for the first course, for the
VARK learning style traits-C1
Fig. 1.Correlation coefficient of two sample groups for the second course, for
the VARK learning style-C2
For the second course C2, see Fig. 4., the difference is far
more significant where we can conclude on how learning styles
do affect the test outcomes. In this case the first sample Group
A the VARK strategy (V=-0.08; A=-0.12; R= -0.02; K=0.10)
has values of the correlation coefficient which demonstrate a
negative correlation. For the second group B the correlation
coefficient is in a far higher positive range (V=0.42; A=0.32;
R= 0.26; K=0.44).
The data analysis of the correlation coefficient implies that
the achievement of online learning has no significant relation
with learning styles. The values of the coefficients are to small-
scaled so that we can identify a telling correlation between
VARK learning styles and test results. Nevertheless, it is
important to consider that, even if a specific student learns best
in a certain way, he or she should be adopted to a variety of
learning experiences to become a more adaptable online learner.
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B. QoE of the two classes
Fig. 5.Combined QoE distribution for the two online courses
Quality of Experience is the measurements of how students
felt and was satisfied during the online classes. Based on the
study flow students had to declare how they agreed with the
overall experience they underwent during the two online courses
(6 absolutely agree to 1 absolutely disagree).
The percentage presented in fig. 5 shows that students had
an overall good experience during this experiment. Unrelated to
the test results, the students overall experience with the two
online courses was satisfying.
VI. DISCUSSION AND CONCLUSION
This paper is a pursuit to better understand the online
teaching approach through the inquiry of learning styles. Our
achieved results indicate that learning styles are not significantly
related with the achieved test results how students attend and
finalize online courses. More exactly we found that learning
styles differ while attending online classes and based on our
findings students prefer kinesthetic as the most favorite learning
style.
The role of learning styles in education should be considered
in the aspect of teaching delivery preferences and examination
the ability to explain learning outcomes in online environments.
The correlation coefficients for almost all our analyses were
positively related with the sample groups which had the
opportunity to choose the preferred way of delivering, but even
though the value of the coefficient in all cases was in a range
which shows up not a compelling role on test results. In our
view, as technology matures, online education will experience
important changes with respect to the type of electronic
interactions between learners and instructors, further rising the
difficulty of this delivery method. Such advances in complexity
propose different sets of behaviors from learners to exploit
results.
We view this research attempt as a potential area for more
investigations and as a crucial part to make online education a
more impressionable environment for the significant growth of
student’s shifting to these settings.
Finally, these results could help to better adapt and
consolidate online classes, so that teachers are more aware of
what students are looking in for while attending online classes,
and so adjust to their needs and preferences to achieve the
highest results. Based on the results of the QoE it proves that
students do like online classes and their experience is positively
related with the quality of those classes attended.
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Background Students use social media for sharing information and connecting with their friends, also for peer support, peer learning and student engagement. Research indicates that approximately twice the number of students were using social media for educational purposes compared to academic staff and almost all students discuss academic issues on social media. However, little is known about how diverse cohorts of student nurses use social media for specific purposes at different stages of their learning. Objectives Identify how student nurses in each country of study use social media for learning. Identify how each generation of student nurses use social media for learning. Identify how student nurses use social media as their education progresses. Design A cross-sectional survey. Settings The study was undertaken across three countries Jamaica, Trinidad and Tobago and the UK. Participants Student nurses from each of the countries that consented to participate met the inclusion criteria. Methods 1050 student nurses across the three countries self-completed the cross-sectional survey between March and September 2019. Data was analysed using descriptive and inferential statistics. Results WhatsApp® was the most used platform for learning amongst participants. Watching videos and downloading articles represented two-thirds of social media usage for learning. Smart phones were the most used device to access social media. Kruskal-Wallis tests were significant (≤0.001) for checking social media and messaging in lecture, use of social media for studies and classroom activities by country, generation (except classroom activities) and year of education. Use of social media for classroom activities had no significance by generation. Conclusion Country, generation and year of education are factors that influence the use of social media in student nurses' learning. These should be considered by Universities in curriculum development and in teaching and learning delivery. From a pragmatic approach, social media is available and used by a majority of student nurses and can be widely assimilated into the nursing curriculum.
... Due to pandemic COVID-19, the learning process should be done at home through internet connections which is called as online learning (Idrizi et al., 2018). The government changed face-to-face learning into online learning. ...
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This study aimed to identify study habits during the online learning and face to face learning, the dominant learning styles of the students, find out whether there are differences of study habits in online learning and face-to-face learning, the association between learning styles and study habits, and the constraints of EFL students to have good study habits in online learning. The design of the study was embedded mix method research design. The sample of the study was 205 students in Hotel Department in SMKN 4 Denpasar who were chosen randomly. The data were collected by using close-ended and open-ended questionnaires. The result of the study showed that there were differences between study habits in online learning and face-to-face learning. In online learning, students had moderate study habits, while in face-to-face learning they had good study habits. The dominant learning styles were kinesthetic and there were low associations between learning styles and study habits. The were some constraints to have good study habits from parents, teacher, students and school.
... The effects of their study were evaluated at the final exam. In order to inspire students to engage thoughtfully, they received extra credit for their grades depending on their results [14]. This research attempted to establish a connection gender influence student academic success when taking online courses. ...
Conference Paper
Online learning is proving to be a new milestone in education, the advantages that were known from the beginning are now days more appealing and important than ever, the opportunity to learn and teach not depended on location and time and has brought online learning in front of each education institution. We will focus on one dimension of how online learning impacts academic success based on gender. Gender has always been an interest since we assume that male and female students vary in their environment of learning and especially online, with a promising advantage for male students since it is assumed that male students would be better handling the technical part, than female students. We conducted two online courses on the Moodle platform, and observed how gender will influence students' outcomes. The initial results show little differences based on gender where male students slightly have higher results in only online courses while female students show higher overall success for the Faculty of Computer Science.
... At a glance about the development of research or knowledge about the learning style, the more days are also increasingly complex related descriptions in each learning style. In accordance with the statement [18] gives a review and explains a more complex learning style that is a style of learning VARK (Visual Audio Read/Writing Kintesthetics), is a learning style by classifying an individual to 4 categories, namely: (1) visual; (2) audio; (3) read/writing; and (4) kinesthetics. The etymological and terminological aspects of VARK is the same as other learning styles, only adding a new style that was previously not disclosed, namely read/writing. ...
... The first course (C1) can be regarded as a less sophisticated course on computer science, while the second course (C2) is a more sophisticated course requiring some prior computer science expertise. Students enrolled in both classes, and the participants were randomly selected [15]. ...
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Online learning is a contemporary concept in which ideas, models, and traditional teachings have changed. We examine how learning style affects the learner's academic achievements in online lessons and gender-specific environments. This research investigates whether VARK styles are associated with course results, regardless of the actual VARK results, and whether any study techniques are associated with course results. The results indicate that almost all students did not report on research approaches after their VARK assessment and that the students' achievement is not associated with their results in each VARK category. This provides additional evidence that the usual teaching styles should be redefined for Online classes where the styles corresponding to the willingness of learners are more difficult to be defined.
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This article examines the impact of personality traits, learning styles, gender, and online course factors (course difficulty, group affiliation, provided materials, etc.) in the academic success of students taking online courses and their overall success rate through traditional classes. Students’ performance in the online learning environment is still a new perception, and a fair numbers of details are still unknown, in stark contrast to the details known in regard to traditional learning methods. Different types of learners respond differently to online and traditional courses. A case study was performed in which students were asked to attend two online courses, with different difficulty levels, during one semester. One-way analysis of variance was used to determine which factors are significant for the academic performance of students taking online courses, as well as for their overall academic success. Findings from the case study indicate that female students score slightly better, course difficulty has impact on test results, emotional students are more susceptible to online environments, and learning styles are more difficult to identify in online classes.
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This study seeks to explore what factors influence students’ ICT use and web technology competence. The objectives of this study are the following: (a) To what extent do certain elements of Rogers’ (2003) Diffusion of Innovations Theory (DOI) explain students’ ICT use, (b) To what extent do personality characteristics derived from the Big Five approach explain students’ ICT use, and (c) To what extent does motivation explain students’ ICT use. The research was conducted in Israel during the second semester of the academic year 2013-14, and included two groups of participants: a group of Educational Technology students (ET) and a group of Library and Information Science students (LIS). Findings add another dimension to the importance of Rogers’ DOI theory in the fields of Educational Technology and Library and Information Science. Further, findings confirm that personality characteristics as well as motivation affect ICT use. If instructors would like to enhance students’ ICT use, they should be aware of individual differences between students, and they should present to students the advantages and usefulness of ICT, thus increasing their motivation to use ICT, in the hopes that they will become innovators or early adopters.
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MOOCs are open, online courses that use information technologies to enhance the learning experience and attract various people from the entire world. The current study uses the Technology Acceptance Model (TAM), as well as personal characteristics such as learning strategies, cognitive appraisal, and Kuhlthau’s (1991) model of information seeking as theoretical bases for defining factors that may influence students adopting MOOCs in their learning process, as well as describe their feelings during the learning process. The study was conducted in Israel during the 2014 academic year, and used both quantitative and qualitative techniques and involved 102 students who participated in a MOOC as part of the requirements in an offline course. They were requested to keep study diaries. The quantitative analysis revealed that perceived usefulness (PU) and perceived ease of use (PEOU) have a major influence on the intention to enroll in a MOOC. PEOU can be increased by improving the current MOOC platforms. PU can also be improved by providing content that suits the students’ needs. The qualitative analysis showed mood changes over time; the feelings of uncertainty were replaced by expressions of confidence. We found that students have different needs and expectations. Therefore, the MOOC’s platforms should provide multiple options to accommodate these needs.
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Objective of the study was to assess selected principles of effective online education. Elements of those principles were identified and ranked in terms of their relative importance through Delphi procedures. Research steps included (1) a review of relevant literature critically reporting challenges and credibility of online course delivery experienced in the higher education, (2) developing a list of major principles for online learning (efficacy, student empowerment, and academic integrity) based on the literature, (3) selecting a sample through a chain-referral technique of faculty members and supporting technology staff involved in online teaching at selected university campuses, (4) interviewing respondents in two rounds to rank goals and means of each of the three evaluative principles, and (5) analyzing data and subjecting them for reliability and validity analyses. The study found strong academic support in the matters of efficacy and student empowerment for online teaching; but also found some concerns respondents had about the issues of maintaining adequate integrity of online courses. Keywords: online education, teaching-learning process, identifying three effectiveness evaluation principles of efficacy, student empowerment and academic integrity; ranking goals and means for three principles through Delphi method, reliability, validity
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Distance education learning environments provide tremendous convenience and flexibility, allow-ing busy, mobile adult learners to engage in education while coping with their limited resources in terms of time, energy and finances. Following a student-centered approach this study investi-gates adult students’ subjective perceptions while using distance education systems based on a videoconferencing platform as Quality of Experience (QoE). Based on a literature review, socio-logical behavior and expectations, we have constructed a structural equation model (SEM) illus-trating relations among different variables that can predict positive levels of adult students’ QoE, thus providing guidelines for proper development. We have tested the model using a survey of 198 primary education school teachers involved in a videoconferencing-based learning program for teacher enhancement. Results show a good fit to the model developed. The analysis showed that adult students’ QoE is directly influenced by appropriateness of teacher-student interaction and ease of participation, as well pre-dicted by students’ motivation to attend similar trainings. Additionally, we found that variances in technical quality did not directly influence their QoE from the learning sessions.
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