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The study presented in this paper sought to explore several dimensions to online learning. Identifying the dimensions to online learning entails important basic issues which are of great relevance to educators today. The primary question is "what are the factors that contribute to the success/failure of online learning?" In order to answer this question we need to identify the important variables that (1) measure the learning outcome and (2) can help us understand the learning experience of students using specific learning tools. In this study, the dimensions we explored are student's attitude, affect, motivation and perception of an Online Learning Tool usage. A survey utilizing validated items from previous relevant research work was conducted to help us determine these variables. An exploratory factor analysis (EFA) was used for a basis of our analysis.
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FACTORS AFFECTING ONLINE LEARNING
Dennis Kira
Department of DSMIS, John Molson School of Business, Concordia University
1455 Boul. De Maisonneuve, Montreal, Quebec, Canada H3G 1M8
Raafat Saade
Department of DSMIS, John Molson School of Business, Concordia University
1455 Boul. De Maisonneuve, Montreal, Quebec, Canada H3G 1M8
ABSTRACT
The study presented in this paper sought to explore several dimensions to online learning. Identifying the dimensions to
online learning entails important basic issues which are of great relevance to educators today. The primary question is
“what are the factors that contribute to the success/failure of online learning?” In order to answer this question we need to
identify the important variables that (1) measure the learning outcome and (2) can help us understand the learning
experience of students using specific learning tools. In this study, the dimensions we explored are student’s attitude,
affect, motivation and perception of an Online Learning Tool usage. A survey utilizing validated items from previous
relevant research work was conducted to help us determine these variables. An exploratory factor analysis (EFA) was
used for a basis of our analysis.
KEYWORDS
Dimensions, Affect, Perceptions, Motivation, Learning, Attitudes
1. INTRODUCTION
The opportunities for learning and growth of online are virtually limitless. Internet-based education transcends
typical time and space barriers, giving students the ability to access learning opportunities day and night from every
corner of the globe. In one decade since the coding language for the World Wide Web (WWW) was developed,
educational institutions, research centers, libraries, government agencies, commercial enterprises, advocacy groups,
and a multitude of individuals have rushed to connect to the Internet. One of the consequences of this tremendous
surge in online communication has been the rapid growth of technology-mediated distance learning at the higher
education level.
Education has expanded from the traditional in-class environment to the new digital phenomenon where
teaching is assisted by computers (Richardson and Swan, 2003). Today, we find a vast amount of courses, seminars,
certificates and other offerings on the Internet. This wave of educational material and online learning tools has
challenged the effectiveness of the traditional educational approach still in place at universities and other education
institutions. Consequently, these institutions are struggling to redefine and restructure their strategies in providing
education and delivering knowledge. With today’s student demographics, educational institutions are rushing to
meet the needs of the new learner by designing and setting up online learning tools as support to the computer
assisted classroom.
With the wide use of technology in today’s learning environment, we should not anymore be concerned with
finding out which is better, face-to-face or technology-enhanced instruction (Daley et al, 2001). In fact, student’s
experience with a course does not only entail the final grade but how much of the learning objectives have been
attained. Online learning presents new opportunities to engage more with the students and student-centered
learning, thereby enhancing the learning experience. Our primary goal should be whether students really learn with
the intervention of online learning tools. If yes, what are the variables that contribute to the success of online
learning tools? If no, then what is going wrong and how can we enhance the learning tool in question? To
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understand the process of learning using online learning tools, we need to identify the important variables that
measure the learning outcome of students using a specific learning tool, and also the variables that help us
understand students’ learning experience with the learning tool.
In essence, learning is a remarkably social process. In truth, it occurs not as a response to teaching, but rather as
a result of a social framework that fosters learning. To succeed in our struggle to build technology and new media to
support learning, we must move far beyond the traditional view of teaching as delivery of information. Although
information is a critical part of learning, it’s only one among many forces at work. It’s profoundly misleading and
ineffective to separate information, theories, and principles from the activities and situations within which they are
used. Knowledge is inextricably situated in the physical and social context of its acquisition and use.
From examining previous literature, we identified six variables that are considered to be important by
researchers to the learning outcome and learning experience with online learning tools. These variables are an
affect, a learner’s perception of the course, a perceived learning outcome, an attitude, an intrinsic motivation and an
extrinsic motivation. In this study, a survey methodology was followed. We adopted items (questions) for these
variables from different studies and performed an Exploratory Factor Analysis (EFA) to test the validity of the
variable sets in the present context. It is the objective of this paper to identify those variables that may play a
significant role in learning while using online learning tools.
1.1 Dimensions of online learning
A recent study performed by Sunal et al. (2003) analyzed a body of research on best practice in asynchronous or
synchronous online instruction in higher education. The study indicated that online learning is viable and resulted in
the identification of potential best practices. Most studies on student behavior were found to be anecdotal and are
not evidence based. Researchers today are concerned with exploring student behavior and attitudes towards online
learning. The evaluation of behavior and attitude factors is not well developed and scarce. Motivated by the need for
more concrete and accurate evaluation tools, we identified six important factors that may be used to better
understand student behavior and attitude towards online learning. These factors which we shall refer to as the
dimensions to online learning are affect, perception of course, perceived learning outcome, attitude, intrinsic
motivation and extrinsic motivation.
Affect refers to an individual’s feelings of joy, elation, pleasure, depression, distaste, discontentment, or hatred
with respect to particular behavior (Triandis, 1979). In previous studies, the student’s perceptions of using
technology as part of the course learning process was found to be mixed (Piacciano, 2002, Kum, 1999). Some
students were uncomfortable with the student-centered nature of the course and were put-off by the increased
demands of the computer-based instruction, which reduced student engagement in the course and led to a decline in
student success (Lowell, 2001). Perceived learning outcome is defined as the observed results in connection with
the use of learning tools. Perceived learning outcome was measured with three items: 1) performance improvement;
2) grades benefit; and 3) meeting learning needs. Most of the online learning literature concentrates on student and
instructor attitudes towards online learning (Sunal et al., 2003). Marzano and Pickering, 1997, indicated that
students’ attitude would impact the learning they achieve. Researchers also studied motivational perspectives to
understand behavior. Davis et al. (1992) have advanced this motivational perspective to understand behavioral
intention and to predict the acceptance of technology. They found intrinsic and extrinsic motivation to be key
drivers of behavioral intention to use (Venkatesh 1999, Vallerand, 1997). Wlodkowshi (1999) defined intrinsic
motivation as an evocation, an energy called forth by circumstances that connect with what is culturally significant
to the person. Extrinsic motivation was defined by Deci and Ryan (1987) as the performing of a behavior to achieve
a specific reward. In students’ perspective, extrinsic motivation on learning may include getting a higher grade in
the exams, getting awards, getting prizes and so on.
2. METHODOLOGY
An exploratory factor analysis approach was followed to test the validity of the dimensions of online
learning.
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2.1 The Online learning tool
The Online Learning Tool was developed so that students could practice and then assess their knowledge of
content material and concepts in an introductory management information systems course. The learning tool
helps students rehearse as well as learn by prompting them with multiple-choice, and true or false questions.
The learning tool is web based and can be accessed using any web browser. Selection of the web to
implement the learning tool is appropriate due to the fact that the technology is available from many
locations around the campus, friends, internet cafes and homes, thus access would not count as a barrier to
the usage of the technology.
The learning tool is programmed using html and scripting languages with active server pages (ASP)
support to communicate with the database. The html and ASP files are very simple in design and do not
include graphics and images or any other distracting objects.
2.2 Participants and Procedure
A flowchart describing the suggested students’ learning process with the Online Learning Tool integrated is
shown in figure 1 below. Steps 1 to 5 are a cycle that needs to be followed for every chapter. First, the
student should study chapter C(i) prior to the use of the Online Learning Tool (step 1). Once the student has
studied chapter C(i), he/she can login (via the internet) and select to practice answering questions ‘P(i,j,k)’
associated with the chapter ‘i’ studied, where ‘j’ and ‘k’ represent multiple choice and true or false questions
respectively (step 2). The practicing component prompts the student with a set of five questions at a time.
The student answers the questions and requests to be evaluated. The Online Learning Tool then identifies the
correct from the incorrect answers. The student can verify the results and when ready click on the ‘Next’
button to be prompted with another randomly selected set of questions (step 2). The student can practice as
much as he/she feels is necessary (step 3), after which he/she can do the test for the specific chapter T(i,j,k)
(step 4). The student can then continue with another cycle identified by a new chapter to study and practice
(step 5). At any time, a student can request an activity report which includes a detailed view of what and how
much they practices and a summary report which provides them with running average performance data.
Figure1. The Online Learning Tool process.
Clearly, the procedure is based on students performing repetitive exercises related to subject under
consideration. Repetition is one of the most basic learning techniques. Children use it to learn to speak.
Athletes use it to perfect athletic skills. Repetition is often seen as boring or looked down upon as an attempt
to simply memorize rather than understand. However, for many individuals with learning differences,
repetition is essential. Knowing when huge amounts of repetition are needed is what often makes the
difference between learning and forgetting and learning and remembering.
2.3 Questionnaire
Validated constructs were adopted from different relevant prior research work (Venkatesh et al., 2003,
Agarwal and Karahanna, 2000, Davis, 1989). The wording of items was changed to account for the context
of the study. All items shown in the appendix were measured using a 5-point scale with anchors all of the
questions from “Strongly disagree” to “Strongly agree” with the exception of ‘learners’ perception of course’
Study
Cha
p
ter C
(
i
)
Practice
Q
uestions P
(
i
,j,
k
)
Assess T(i,j,k)
Ready for
test T
(
i
,j,
k
)
?
N
o
Yes
Go to next
Cha
ter
i
Reports on
P
(
i
,j,
k
)
& T
(
i
,j,
k
)
1 2
3
4
5
6
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which had anchors between 0% and 100%. The questionnaire included items worded with proper negation
and a shuffle of the items to reduce monotony of questions measuring the same construct.
3. RESULTS AND DISCUSSIONS
3.1 Student Feedbacks
The Affect reported by the sample students is not positive. More than 50% of the students reported that they
feel the “learning tool” to be a nuisance. 40% of them also reported frustration in using the “learning tool”.
The perception on the course was positive. More than 50% of the students indicated that the learning tool is
important for the course. Closer to 95% of the students felt that they will score above 50% in the course with
half of them expecting a mark above 75%. Close to 60% of the students found that “learning tools” are
helpful for understanding better course content. Also 60% of the students reported the advantages of
“learning tools” overweigh the disadvantages.
The perceived learning outcome is very positive. More than 60% of the students indicated that the
“learning tool” meets their learning needs and does not waste their time. Their understanding of the topic was
improved by using the tool. Close to 50% of the students reported that they understand the strategy of the
“learning tool” and were able to adjust their learning in order to maximize the advantage in using the learning
tool. More than 80% of the students reported that the “learning tool” being a support throughout the semester
motivated them to use it more regularly.
3.2 Exploratory Factor Analysis
Many scientific studies are featured by the fact that “numerous variables are used to characterize objects”
(Rietveld & Van Hout 1993). Examples are studies in which questionnaires are used that consist of many
questions (questionnaire items), and studies in which mental ability is tested via several subtests, like verbal
skills tests, logical reasoning ability tests, etcetera (Darlington 2004). Because of these large numbers of
questionnaire items that are measure, the study can become rather complicated.
3.2.1 Factor Analysis
First, we performed an initial factor analysis to observe the relationship among the factors and their
indicators. Some variables were well defined with a factor (AFF1, AFF2 and AFF3 with Factor 4; PLO1 and
PLO2 and PLO5 with Factor 5). However, other items such as ATT1 loaded on Factor 1 (0.580) and factor 2
(0.578). Factor analysis was performed on the original set of items, six factors were retained initially. After
factor extraction often it is difficult to interpret and name the factors on the basis of their factor loadings.
3.2.2 Retained Solution
Due to the low correlation and low factor loading, the following items are rejected: AFF1, PC2, PC4, PC5,
PC6, ATT6, PLO1, PLO2, PLO4, IM1, and EM1. After dropping these items, the final analysis is presented.
Table 1 summarizes the relationship among the factors and their observed indicators. Items with high values
are bold to contrast the loading on their respective factor. Items that belong together should have relatively
higher loading on the same factor. For example PC1 and PC2 load 0.679 and 0.927 on factor 4, which are
high compared to the other variables which load 0.338 or less on the same factor.
Table 1. Factor loadings on respective items
Variable Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
AFF2 0.155 0.140 -0.877 -0.050 0.122 -0.208
AFF3 0.200 0.006 -0.650 -0.200 0.035 0.147
PC1 0.282 -0.151 -0.073
0.679 0.326 0.079
PC3 0.236 -0.170 -0.095
0.927 -0.005 -0.193
ATT1 0.666
0.240 -0.182 -0.338 0.236 -0.250
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ATT2 0.672
0.116 -0.083 -0.044 0.253 -0.206
ATT3 0.740
0.202 -0.185 -0.212 0.101 0.043
ATT4 0.674
0.142 -0.115 -0.100 -0.008 0.036
ATT5 0.588
0.189 -0.327 -0.214 0.431 -0.060
PLO3 0.144 -0.555 -0.086 0.098 0.297 -0.076
PLO5 0.424 -0.732 -0.143 0.123 0.367 -0.016
IM2 0.176 0.188 -0.034 -0.276 0.335 -0.574
EM2 0.119 0.250 -0.235 0.210 -0.556 -0.123
We can immediately see that the variables are well defined with a factor (PC1 and PC3 with Factor 4;
ATT1, ATT2, ATT3, ATT4 and ATT5 with Factor 1; AFF2 and AFF3 with Factor 3; PLO3 and PLO5 with
factor 2).
4. LIMITATION AND CONCLUSION
Dimensions that influence online learning have been investigated by researcher under different experimental
traits. In this study, we gathered items from different literature and tested the validity of these items under
the use of an online learning tool context. We acknowledge that implications of our findings are only
confined to the limits at which we interpret the results, and that these limitations must be acknowledged.
From the participants’ perspective, bias with the sample of learners may be due to the sample size, and
demographic controls. Moreover, the nature of the course is such that it is an introductory MIS course
containing many chapters and additional topics that we ask the students to learn. This is especially difficult
for the students who have never been exposed to the field of information technology. Therefore generalizing
the findings in terms of behavior and intentions to other courses and schools may be limited. As a result, we
need to identify the boundary conditions of the dimensions as they relate to demographic variables such as
age, gender, Internet competencies and other course properties. In fact, the nature of the course is an
important variable that contributes to the success or failure of online learning. In effect, some courses lend
themselves to be appropriate for online while other do not. Similarly, some students have the skill to follow
online learning tools while others do not.
Considering the questionnaire, it is not free of subjectivity. The respondents’ self-report measures used
are not necessarily direct indicators of improved learning outcomes. Furthermore, although a proper
validation process of the instrument was followed, the fact that the questions were collected from other
research may not necessarily be precise and appropriate in the context of this study. Conclusions drawn are
based on a specific online learning tool usage but not for all online learning tools. Other learning tools can be
designed for different tasks and for different platforms (in this case it was web-based) and this study was
based on a single distinct technology. This however, may not generalize across a wide set of learning
technologies.
The effectiveness of online learning tool in facilitating students’ learning and the learners learning
outcome are measured in many dimensions. In this study, we chose six important dimensions that have been
investigated in different research and tested the validities of these dimension under the current context. These
five dimensions are Affect, Learner’s Perceived on the Course, Attitude, Perceived Learning outcome,
Intrinsic Motivation and Extrinsic Motivation. In this validating process, all the six dimensions show content
and construct validities to some extent. The last two constructs related to motivation should be deleted as
factors if Stevens’ (2002) guideline is followed since there is only one loaded item for these two factors. We
have decided to retain these two factors since other literatures indicate the importance of motivational factors
in learning (Venkatesh 1999, Venkatesh and Davis, 2000, Venkatesh et al. 2002). The unreliable items in
constructs are eliminated and not considered in the final solution of the factor analysis. Student feedback on
questions items and the factor analysis provide
validity of the dimensions that influence the effectiveness of online learning
controls to revalidate under different experimental setups
researchers with the valid questionnaire items to test models or hypotheses under different contexts
hence facilitating the analysis of mediating effects on student experiences and
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quantitative results that may help the researcher/instructor understand the dynamics of the online
learning tool and identify critical element to enhance the tool in helping students perform better in
their learning process
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... If the preparations for such situations were at a satisfactory level, complications raised due to online learning could have been minimized. According to the study of Kira and Saade (2006), the following factors affect the efficiency of online learning: learner's attitude, perceived learning outcomes and intrinsic and extrinsic motivations, while Keskin and Yurdugül (2020) point out that e-learning readiness, cognitive study strategies and motivation are considered as the factors that influence the efficiency of online learning (Table 6). Thus, online learning efficiency affects learners' satisfaction during COVID-19 as mentioned in Hypothesis 6 (H 6 ). ...
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