ISBN: 978-989-8533-63-0 © 2017
WHY DO LEARNERS CHOOSE ONLINE LEARNING:
THE LEARNERS’ VOICES
Hale Ilgaz and Yasemin Gulbahar
Ankara University, Distance Education Center, 06830 Golbasi, Ankara, Turkey
ABSTRACT
Offering many advantages to adult learners, e-Learning is now being recognized - and preferred - by more and more
people, resulting in an increased number of distance learners in recent years. Numerous research studies focus on learner
preferences for online learning, with most converging around the individual characteristics and differences, if not the
features of the technology and pedagogy used. For Turkey, the situation is also similar, with the number of adult learners
who prefer online learning increasing each year due to several reasons. The result of this is an increase in the number of
online programs offered by many universities. Hence, this research study has been conducted to reveal the prevailing
factors causing learners to choose online learning. Through this qualitative research regarding online learners in a state
university, it is found that having a full time job, accessibility and flexibility, individual responsibility, effective time
management, physical distance, institutional prestige, disability are the common factors for under graduate and graduate
learners in their preference for online learning. Awareness of these factors can support the stakeholders while designing
e-Learning from both technological and pedagogical points of view.
KEYWORDS
Online learning, preferences, expectations
1. INTRODUCTION
Offering many advantages to adult learners, e-Learning is now being recognized - and preferred - by more
and more people, resulting in an increased number of distance learners in recent years. Emphasizing that
distance education has a bright and promising future, Zawacki-Richter and Naidu (2016) stress that, “In fact,
there has never been a better time to be in the field of open, flexible, distance and online education than
now!” (p. 20).
The commonly discussed factors that make online learning attractive for adults are: independence from
time and place; accessibility, and; economic reasons. With the MOOC movement, extremely high quality
online courses are now being delivered to learners by many well-known universities. Moreover, many
universities are either providing online programs or courses as a support to traditional instruction, in the form
of blended learning, flipped classes, etc. Indeed, there are almost no universities left who don’t benefit from
these advantages of technology usage and its support in teaching-learning processes.
A variety of reasons might account for these learning preferences. Çağlar and Turgut (2014) attempted to
identify the effective factors for the e-learning preferences of university students; they concluded that,
“Efficient usage of time and reduced educational expenses were found to be on top of the list as the most
valued advantages of e-learning” (p. 46). Moreover, having responsibilities, a full-time job and no access to a
nearby university may also cause learners to prefer online learning.
Among the factors that affect learners’ attitudes toward e-learning, a positive attitude toward technology,
ease of access and use of internet, computer literacy, perceived usefulness, self-efficacy, motivation,
patience, self-discipline, and self-regulation seem to be widespread and the most commonly reported (Liaw,
Huang & Chen, 2007; Nogueira & Machado, 2008; Sun, Tsai, Finger, Chen & Yeh, 2008; Bertea, 2009). On
the other hand, Lim and Morris (2009) examined the influence of instructional and learner variables on
learning outcomes for a blended instruction course and stated that “… age, prior experiences with distance
learning opportunities, preference in delivery format, and average study time are those learner antecedents
differentiating learning outcomes among groups of college students” (p. 282).
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Regardless of learners’ attitudes toward e-learning, instructional design plays an all important role during
an efficient online learning process. From the literature, it can be seen that the most common instructional
design models – such as ADDIE, ASSURE, Dick & Carrey, Smith & Ragan - start with the analysis step.
This step can be broken down into analysis of the learner, content, media and aim. Nevertheless, the question
is: after analysis, are designers really reflecting the possible applications in their instructional design process?
In many online learning programs learner analysis was carried out collecting learners’ general
demographic data. Even if the target group of learners have similar academic backgrounds, these learners
tend to have very different individual properties (Navarro & Shoemaker, 2000; Conrad & Donaldson, 2010),
expectations (Dabbagh, 2007; Moskal & Dziuban, 2001) and motivation (Keller & Suzuki, 2004; Kearsley,
2002) levels. Therefore, after enrollment, institutions or practitioners should conduct a deep learner analysis;
this also influences the quality of instructional design in a holistic way. Thus, institutions can aim to decrease
the drop-out rates (Park & Choi, 2009; Chyung, 2001), increase the attendance (Yudko, Hirokawa & Chi,
2008; Rovai, 2003) and, in general terms, maintain a more efficient learning process.
Numerous research studies have focused on learner preferences for online learning, with most converging
around the individual characteristics and differences, if not the features of the technology and pedagogy used.
A similar situation is seen in Turkey, with the number of adult learners who prefer online learning increasing
each year due to several reasons. The result of this is an increase in the number of online programs offered by
many universities. For this reason, the current research study has been conducted to reveal the prevailing
factors causing learners to choose online learning. Thus, this research seeks answers to the following research
questions:
1.
2.
What are the factors that affect students’ preferences for online learning?
Are there any differences between program types in terms of student preferences?
2. METHODOLOGY
2.1 Research Design
This research is designed as a qualitative study. Participants were requested to answer two online open-ended
questions at the beginning of fall semester, and asked underlying reasons for their choice of online learning
method, and their expectations about online learning.
2.2 Participants
Participants of this study were the online learners of a state university who were enrolled in various
e-learning programs. These programs were composed of six undergraduate degree and four graduate degree
programs. Most of the online learners were females (55%), married (59%) and aged 18-25 (41%). Detailed
demographics for the participants are presented in Table 1.
Table 1. Participant demographic data
Female
Male
Single
Marital Status
Married
18-25
26-33
34-41
Age
42-49
50 and up
Total
Gender
Undergraduate
f
%
1278
59,92
855
40,08
1032
48,38
1101
51,62
29
9
136
41
112
34
45
14
7
2
2133
100
Graduate
f
%
184
55,93
145
44,07
133
40,43
196
59,57
860
41
761
36
398
19
80
4
18
1
329
100
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2.3 Data Analysis
After checking all of the responses, it was found that 944 participants from undergraduate level and 178
participants from graduate level were suitable for data analysis. The collected data was coded separately by
the researchers. None of the qualitative data analysis software has been used, because of not missing any
statement. In this research, coding was conducted according to the participants’ comments, and the codes and
themes were generated by the researchers.
A member checking validation strategy was used in this research for validity (Creswell, 2007), and also
an intercoder agreement strategy was used for reliability. Two different coders - apart from the researchers analyzed the codes and themes for a second time. For this dataset, Cohen’s Kappa coefficient was calculated
and found to be 0.90, which is within the range of acceptability (Krippendorff, 2004; Landis & Koch, 1977).
In terms of member checking, researchers called (via phone) 10 randomly selected participants, and talked
about their online learning experiences and reasons for their preferences. During meetings they emphasized
the similar preferences for online learning.
3. RESULTS
3.1 Undergraduate Students
After the qualitative analysis, researchers identified 12 themes within the undergraduate students’ data. The
themes for undergraduate level are presented in Table 2.
Table 2. Themes for undergraduate students
Themes
Having a full time job
Accessibility and flexibility
Individual responsibility
Effective time management
Individual difficulties
Features of learning environment
Physical distance
Academic preference
Having a second degree
Institutional prestige
Aging
Disability
Total
f
441
218
113
106
83
82
43
23
16
10
8
8
1151
%
38,31
18,94
9,82
9,21
7,21
7,12
3,74
2,00
1,39
0,87
0,70
0,70
100
According to the data analysis, having a full time job is the most significant theme regarding the student’s
reasons for their preferences. They stated that the desire to run their work life and education together, and
also the high tempo of work life forcing them to choose distance education programs. The majority of
students were between 26 and 41 years of age, this data also proves that these students can be active workers
in life. The students stated their situation, as is seen in the example below:
“I am working, and my age is 35. Still, I can complete my education into my area of
interest, and have a diploma via distance education.” [P-722]. “I am working, and I don’t
have any time for traditional learning programs. I choose this program, because it was the
only way for me to learn.” [P-715].
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The other emerging theme was that of accessibility and flexibility. The nature of distance education is that
it is independent from location and time, which are also important criteria in terms of students’ preferences.
“Distance education gives me a large choice of time and location, so I don’t need to be at
an exact place and time. Also, I can continue to my other diploma program which I
enrolled in before.” [P-23]. “It’s very easy to access and the practical, discretionary
participation feature to the synchronized sessions is very important for me. Also, the
opportunity of listening to sessions from records, and from different lecturers makes me
choose distance education.” [P-92]. “I choose distance education, because I can study
whenever I want. I can listen to session recordings and there isn’t an obligation about
attending synchronized sessions.” [P-373].
Another characteristic of distance education students is that, generally, they couldn’t complete, or even
start, their education because of their individual responsibilities. This situation can be seen from the codes
and themes emerging from the data. Most of the students stated that they have to take care of their family and
children, or even a relative such as a nephew, or their grandparents.
“I had to choose distance education, because there is no one to take care of my nephew.”
[P-53]. “I am married, and have 3 kids. I really appreciate that this opportunity is provided
to us.” [P-491]. “I choose distance education because I am married and have 2 kids. My
kids are going to elementary school, so they need me at home.” [P-592].
According to the analysis, a point will soon be reached where the large majority of students are likely to
enroll on a distance education program, as this enables them to manage their time very efficiently, and also
handle with family and work responsibilities as well.
Financial problems and being in a prison are addressed in the individual difficulties theme. Students
stated that living far away from the university can cause a high level of transportation, accommodation and
educational expenses for them. As a solution to such potential financial issues, they prefer distance education.
In addition to this, students who have been in prison stated that continuing their education through distance
education is a huge disadvantage for them even if in their circumstances.
After analyzing the students’ data, researchers found that students consider distance education as
systematic, coordinated, repeatable, offering good interaction with teachers, enabling participation from
home, creating the chance for individual work, containing visual-audio presentation techniques, and offering
virtual classroom activities. All of these specifications are considered in the features of the learning
environment theme. Physical distance, having a second degree, institutional prestige, aging and disability
themes also emerged from the qualitative data. Students stated their reasons as follows:
“I have a physical disability; as a result of this, transportation is a problem for me. So, I
choose distance education” [P-522]. “I am a congenitally hearing disabled person; with
distance education I can listen to my courses over and over” [P-840]. “The city I lived in
doesn’t have my program’s formal version” [P-121]. “I am travelling a lot because of my
job, so I have to be in different cities most of the time” [P-327]. “The appealing factor for
me was the university’s prestige. Having a diploma from such big university is very
important for me” [P-878]. “I lost the chance to go to university years ago. I believe that
learning should be from birth to death. Now I am at the age of 35, and continuing my
education at this age makes me happy” [P-911].
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3.2 Graduate Students
After analyzing the graduate students’ data, 8 themes arose. Compared with the under graduate students’
themes, it was found that there were 7 common themes, and only 1 of these was different from the others.
These themes are presented in Table 3.
Table 3. Themes for graduate students
Themes
f
%
Having a full time job
Effective time management
Accessibility and flexibility
Lifelong learning
Physical distance
Individual responsibility
Institutional prestige
Disability
Total
90
42
26
24
13
7
1
1
204
44,12
20,59
12,75
11,76
6,37
3,43
0,49
0,49
100
The lifelong learning theme consisted of students’ wishes about increasing their academic knowledge,
and providing professional development. Within the context of these aims, they stated that the reasons for
their preferences as:
“Distance education provides me with continuing education, and I’m improving myself
academically as well as in my work life” [P-13]. “I believe in lifelong learning, but I am
dealing with a high tempo work life. I couldn’t attend a traditional program because of my
workload, so I choose distance education. Distance education is a very useful system for
busy people like me” [P-46]. “I choose distance education because it was the most
appropriate method with which I can continue with minimum loss elsewhere. Besides, I
believe that, after completing this program, I will be in a better position in my work life”
[P-53].
When looking over the order of the themes, having a full time job was the most important, as was the case
in the undergraduate program students’ data. Effective time management, and accessibility and flexibility
were the next themes in terms of importance. Also being married, having children, living outside of the city
or country, and being a part of a leading university were the other reasons mentioned.
4. CONCLUSION
The results of this study indicate the importance of distance education, which can provide the equality of
opportunity independent of graduation level. Every person has the right to obtain a quality education,
regardless of whether it is a graduate or undergraduate degree. Sometimes life obstacles can be a barrier in
front of people’s choices. In this study, the researchers aimed that identify the differences between students’
reasons for their preferences for distance learning. It was found that, generally, these reasons were parallel
between these two degrees, but also there were some differences regarding certain points.
The common themes for both of the groups were having a full time job, accessibility and flexibility,
individual responsibility, effective time management, physical distance, institutional prestige, and disability.
The differences were in terms of preferences at graduate degree level, individual difficulties, features of the
learning environment, academic preference, obtaining a second degree and the aging process. For graduate
students, the predominant difference was the desire for lifelong learning. Actually, these themes tend to
represent the students’ characteristics. Undergraduate degrees are fundamental for finding a job, so this is an
obligation for most students. Because of this, people who have difficulties regarding their budget, health
issues or special conditions prefer distance education to a greater extent. Similar difficulties aren’t observed
at graduate level. Graduate level is not an obligation for a job; it depends much more on intrinsic motivation.
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This is why these seven themes weren’t evident in the data analysis. According to the analysis, people who
enroll on a graduate level program are seeking more professional development.
According to both qualitative and demographic data, those people who can’t complete or even start their
education due to family responsibilities are, generally, the female students. Consequently, with distance
education female students are able to find their place in social and work life much more effectively than
before. Social roles and/or cultural expectations can bring about certain disadvantages to females, but it is
shown that distance education can play an important role in overcoming these issues.
Hence, although this research does not add any specific new findings to the field, it was important to
revisit the underlying factors influencing learner preferences, since technology and pedagogy should be
shaped according to these needs. Providing education services to all the people who need them, and also
increasing the quality of education in an accessible way provides numerous benefits to people’s lives. With
the use of regular tracking systems, educational practitioners can better understand students’ reasons for
preferring distance learning, as well as their expectations. Thus, institutions can provide a more enhanced and
comprehensive service.
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TEACHING QUANTITATIVE COURSES ONLINE:
ARE LEARNING TOOLS OFFERED BY
PUBLISHERS EFFECTIVE?
Mohammad Ahmadi, University of Tennessee-Chattanooga
Parthasarati Dileepan, University of Tennessee-Chattanooga
Kathleen Wheatley, University of Tennessee-Chattanooga
ABSTRACT
In recent years, online teaching has become extremely popular. Most institutions of higher learning
are offering online courses in almost every field of study. Teaching any course online is challenging, but
teaching quantitative courses, such as operations management, management science, statistics, and others,
have added a more challenging dimension to online teaching. Publishers have been assisting professors
of quantitative methods courses by developing various teaching and evaluation tools. This study explores
one such publisher’s tool, Quiz Me Mastery Points, of Pearson’s MyOmLab. The performance of students
on their examinations and the Mastery Points they earned through the Quiz Me feature were compared,
and it was determined that there was a significant correlation between the two.
Keywords: Online teaching, Quantitative courses, Quiz Me Mastery Points, MyOmLab
INTRODUCTION
In the last decade online teaching and learning
has become the norm in many institutions of higher
learning. Numerous institutions are offering online
courses both nationally and internationally. The
Online Consortium tracks online education in the
Unites States and releases an annual report entitled
The Online Report Card. The most recently
released report (Allen & Seaman, 2016) showed
there were more than 5.8 million students in the
United States enrolled in one or more online courses
in the fall of 2014. This constitutes 28.4% of all
student enrollment. The report further stated that
many academic leaders (63.31% in 2015) strongly
believe online learning is a critical component of
their long-term strategy. It also stated that 77.14%
of the chief academic officers in 2015 rated the
learning outcome of online education as good as
or better than face-to-face. However, an alarming
finding was that only 29.1% of the chief academic
officers believed their faculty accepted the value
and legitimacy of online education. These findings,
along with historic trends, reveal a mismatch
between the growth in student demand for online
course offerings and the hesitancy of faculty to buy
into the efficacy of online teaching. Reconciling
this mismatch is critical to realizing the full
potential of the online classes that the students are
increasingly expecting.
Data were collected from students in an online
MBA program (Kim, Liu, & Bonk, 2005) through
semistructured, one-on-one interviews, surveys,
and in-person focus group interviews. It was determined that over 70% of those surveyed described
their online learning experience in a positive
manner, and about 93% of the respondents were
satisfied with the quality of their online courses. A
study that conducted one-on-one interviews with
fifteen experienced e-learning instructors (Bailey
& Card, 2009) identified eight effective pedagogical
practices for effective online teaching: fostering
relationships, engagement, timeliness, communicJOURNAL OF EDUCATORS ONLINE
ation, organization, tech-nology, flexibility, and
high expectations. The challenge of understanding
and integrating these eight facets of effective online
teaching was a possible reason for the hesitancy
within the ranks of the faculty to embrace online
teaching (Allen & Seaman, 2016).
Two key obstacles for effectively teaching an
online class were identified as meeting the student’s
core educational needs and maintaining a sense of
teaching presence (Carliner & Shank, 2016). To
meet students’ core needs, instructors must draw
on a variety of tools and strategies, which various
textbook publishers are increasingly offering.
Among them are MyLab by Pearson, MindTap
by Cengage, and Wiley Plus. Effective use of
these tools can bridge the gap between student
expectations and the hesitancy of faculty to meet
the core needs of students.
This paper explores and evaluates the Quiz
Me Mastery Points of Pearson MyOmLab and
determines whether this feature can bridge the gap
between faculty hesitation and student demand for
online offerings. We studied students’ performances
on tests and the Mastery Points they earned
through the Quiz Me feature and found that there
is a significant correlation between the two. First,
we present a comprehensive review of the current
literature that deals with various challenges faced
by online course offerings and what pedagogical
responses were likely to be successful. Then, in
the methodology of the study we investigate the
performance of 174 students over four semesters
(3,000 individual assessment scores). Next, we give
the results of the analysis and we identify factors
that improve or do not have an impact upon student
performance. Finally, we propose possible avenues
for future research.
LITERATURE REVIEW
In recent years, blended teaching and learning,
which includes online versus face-to-face, has
grown immensely; yet, the literature is not as
abundant as one would expect. Not only has
learning been under scrutiny, but some studies
have focused on other students’ and teachers’
viewpoints such as satisfaction, performance,
professor-student interaction, and a host of other
facets of teaching and learning. Smith and Bryant
(2009) observed the paucity of literature on teaching
case-based statistics classes and offer useful
JOURNAL OF EDUCATORS ONLINE
tips for guiding online discussions. Dotterweich
and Rochelle (2012) also lamented the paucity
of research examining student characteristics
and factors leading to successful outcomes.
They studied three modes of delivery (online,
instructional television, and traditional classroom)
with three groups of students with similar GPAs
prior to taking their statistics courses. They found
online students were significantly older and more
likely to repeat the course and have earned more
credit hours prior to enrolling. They also found
that GPA and percentage of absences were highly
significant predictors of course performance. On
the suitability of online delivery for quantitative
business courses, specifically business statistics
and management science, research findings
suggest that features involving professor-student
interaction are the most useful, features promoting
student-student interaction are the least useful and
discussion forums are of limited value in learning
quantitative content (Sebastianelli & Tamimi,
2011). Katz and Yablon (2003) examined students’
academic performance in a required first-year
university internet-based Introduction to Statistics
course and the psychopedagogical variables
that contributed to students’ online learning
as compared to the learning of students who
participated in a traditional lecture-based course.
They found no difference in the performance
levels achieved by students of the two groups.
In addition, they found that participation in
the online course improved psychopedagogical
attitudes towards online learning despite the initial
misgivings of the participants in. A meta-analysis
of performance differences between online and
face-to-face undergraduate economics courses in
the United States (Sohn and Romal, 2015) found
statistically significant and stronger performances
for face-to-face instruction. Further, the study
found older/mature online instruction enrollees
performed better. Concerning satisfaction, a survey
of students of an online statistics course found
positive satisfaction with a mean of 4.00 in a fivepoint Likert-scale (Al-Asfour, 2012). The study
demonstrated that students were satisfied with online
instructions, communications, and assessments.
On the question of students’ perceptions of
online homework assignments, a study of an
introductory finance class discovered that, in
general, students preferred online homework
to traditional homework. The study further
determined that students found that the homework
assignments increased their understanding of the
material and graduate students reported a higher
level of satisfaction than did undergraduates
(Smolira, 2008). Law, Sek, Ng, Goh, & Tay (2012)
examined students’ perceptions of the use of the
Pearson’s online learning platform MyMathLab
as a supplementary tool in conducting assignment
and assessment in a mathematics course and found
that overall the students were satisfied with the use
of the MyMathLab platform.
Alrushiedat and Olfman (2013) conducted a
field experiment that explored the potential benefits
of asynchronous online discussions for business
statistics classes and found they facilitated more
and better-quality participation and engagement
for undergraduates.
Walstrom (2014) compared the performance
and satisfaction of over 220 students enrolled
in a traditional face-to-face class and over 300
students in an online class while migrating an
Electronic Business Management course from
a traditional face-to-face delivery to an online
delivery across a six-and-a-half-year period. The
comparison revealed that student performance
and satisfaction remained mostly consistent across
delivery methods.
Nicholson and Nicholson (2010) surveyed
student and faculty perceptions of using streaming
video for teaching students Microsoft Excel and
Access skills in an introductory management
information systems course. The results from
the survey showed that the use of a multimedia
component to convey course material provided
benefits to students in the form of greater satisfaction
with the learning process, a greater understanding
of the material, as well as a reduction in the effort
required to complete homework assignments. They
further reported that the instructors experienced
a marked reduction in visits from students who
required additional exposure to previously
covered material, a decrease in prep time during
subsequent semesters, and seamless portability to
online learning contexts.
Fuller and Bail (2011), using an action research
model, described the outcomes of an interactive
team-teaching model while teaching an online
graduate-level disaster research and statistics
course during a span of five semesters. They
reviewed instructor reflective logs and student
responses to the team-teaching model and found
that there was a positive benefit in developing
synergy in content and pedagogies, continued
instructor learning, and continuous reflection on
instructional design. They further found that the
immediacy of feedback and the added access and
clarity of the team-teaching process resulted in
students reporting a greater understanding of the
research and statistical process.
Hegeman (2015) examined whether student
performance in an online College Algebra course
could be improved if instructor-generated video
lectures were used instead of publisher-generated
educational resources. The study involved a College
Algebra course that used all the publisher-generated
educational resources and another course in which
students completed instructor-generated guided
note-taking sheets while watching instructorgenerated video lectures with publisher-generated
learning aids available as supplemental resources.
The results of this study showed that strategically
placing instructor-generated content improved
student performance significantly on both online
and handwritten assessments. The effectiveness
of the videoconferencing software Blackboard
Collaborate for carrying out instruction at
the college level to students attending classes
synchronously at multiple locations was evaluated
by Tonsmann (2014) and found to be an effective
method for educating students at a distance.
A multiple regression analysis used a dataset
that included over 5,000 courses taught by over
100 faculty members over a period of ten academic
terms at a large, public, four-year university
(Cavanaugh & Jacquemin, 2015). This study
revealed a statistical difference among course
formats that amounted to a negligible difference
of less than 0.07 GPA points on a four-point scale.
The authors further found an interaction between
course type and student GPA, indicating that
students with higher GPAs performed even better
in online courses. Alternatively, struggling students
performed worse when taking courses in an online
format compared to a face-to-face format.
Pena-Sanchez (2009) examined whether the
course delivery method, online or face-to-face,
and gender affected academic progress. Through
chi-square tests, it was found that the population
proportion of successful students in a course of
JOURNAL OF EDUCATORS ONLINE
Business Statistics did not depend on their gender
or the delivery mode of the class.
Wiechowski and Washburn (2014) studied more
than 3,000 end-of-semester course evaluations
collected from 171 finance and economics courses
in the 2010-2011 academic year. They reported
that the online and blended courses had a stronger
relationship with high course satisfaction than did
face-to-face courses. Further, they stated that there
was no significant relationship found among student
learning outcomes and the mode of course delivery.
Peng (2015) used an ordinary least squares
regression model to analyze a sample of 206 students
during the period from 2008 to 2012 and found that
significant predictors of student performance were
age, major, degree obtained, and the number of
hours a student worked but not the choice of a more
readable textbook.
Calafiore and Damianov (2011) used the online
tracking feature in Blackboard (Campus Edition)
to retrieve the real time that each student spent in
the course for the entire semester and to analyze the
impact of time spent online, prior grade point average
(GPA), and other demographic characteristics of
students on their final grades. They found that both
time and GPA were significant determinants of the
final grade.
Chen, Jones, and Moreland (2010) surveyed
students in online and traditional classroom sections
of an intermediate-level cost accounting course on
several items related to instruction and learning
outcomes. Then, they compared the student
examination performance in the two types of
sections. They found that both learning environments
generally had similar ratings. However, where there
was a difference, the satisfaction level of students in
the traditional classroom was higher. Furthermore,
they stated that the examination performance for 14
of 18 topic areas were similar with the traditional
method producing better comprehension in three of
the remaining four areas.
METHODOLOGY
The opportunities thrown open by the
increasing popularity of online courses comes
with difficult challenges. They include technical
challenges such as mastering software platforms
for content delivery, interacting with students,
online content delivery, participation, assessment,
learning style, time management, and motivation.
JOURNAL OF EDUCATORS ONLINE
There are technical solutions for many of these
challenge and publishers offer learning platforms
for popular textbooks.
Quantitative courses present tough challenges
when they are offered online. Mastering
quantitative aspects of problem solving is critical.
Publisher online platforms have modules that
provide the opportunity for students to practice
and master concepts before taking tests. Pearson’s
MyOmLab platform includes several tools that can
be used for practice and learning concepts as well
as assessments. They include Practice, QuizMe,
Homework, Quiz, and Test.
As students work on each section of the
chapters of the textbook and achieve a minimum
score in a combination of assessment tools set by
the instructor, the students earn a Mastery Point.
In this study, three tools were used: Practice,
QuizMe, and Chapter tests. Students can learn
concepts and problem-solving skills by using the
practice tool, which allows students to seek help
from a variety of sources including reaching out
to the instructor. The QuizMe tool allows students
to self-test at the level of mastery achieved by
using the practice tool. In this study, we set the
minimum threshold of 80% in the QuizMe for
students to earn the Mastery Points associated
with the section. If a student failed to achieve
the minimum score, she or he could go back to
Practice and then retake the QuizMe until earning
the Mastery point. In as much as students can
seek help while using Practice and repeat QuizMe
unlimited times, Mastery Points earned had half
the weight of chapter tests that were similar in
content, but students could not receive any help
and had only two attempts with the higher of the
two grades recorded.
One of the research questions we faced was
whether this process of earning Mastery Points
with unlimited trials of Practice and QuizMe
was helping student performance as measured by
chapter tests. Further, we had both undergraduate
and graduate classes in the pool of classes for
which we gathered data (further described in
the next section). Therefore, we formulated the
following four hypotheses:
Hypothesis 1:
H0: The Mastery Score in a given chapter
does not have any effect on the test score in the
corresponding chapter.
HA: The higher the Mastery Score in a given
chapter the higher the test score will be in the
corresponding chapter.
Hypothesis 2:
H0: The time spent earning Mastery Score in
a given chapter does not have any effect on the test
score in the corresponding chapter.
HA: The higher the time spent earning Mastery
Score in a given chapter the higher the test score
earned in the corresponding chapter.
Hypothesis 3:
H0: The average chapter test scores for graduate
students are the same as the corresponding average
for undergraduate students.
HA: The average chapter test scores for graduate
students are higher than the corresponding average
for undergraduate students.
Hypothesis 4:
H0: There is no interaction
course level and Mastery Score
average chapter test scores.
HA: There is an interaction
course level and Mastery Score
average chapter test scores.
effect between
earned on the
effect between
earned on the
Our study included 174 students over a period
of four semesters. For each of the 174 students,
data were collected on five variables for each of the
nine chapters listed in Table 1. These variables are
shown in Table 2. Note the Mastery Score recorded
was the percentage of total mastery points available
for the given chapter. Similarly, the test scores were
converted to a 100-point scale for consistency.
Table 2. Variables for the Nine Chapters
Variable
Course level
Description
Variable
Graduate or Undergraduate
Course level
Assessment chapter
Chapter
Mastery Score
Percentage of subsections of
the chapter mastered
Mastery Score
Mastery Time
Time spent mastering the
chapter
Mastery Time
Test score (0–100)
Test Score
Description
Variable
Graduate or Undergraduate
Course level
Assessment chapter
Chapter
Percentage of subsections of
the chapter mastered
Mastery Score
Chapter
Test Score
Variable
Course level
Chapter
Mastery Score
THE DATA
We chose Operations Management at the
undergraduate level and Production and Operations
Management at the graduate level. While there were
significant differences in the range and coverage
of topics between the undergraduate and graduate
classes, we identified nine core chapters that were
common to both levels of classes. They are given
in Table 1.
THE RESULTS
The summary of results is presented in Table
3. Figure 1 shows a scatter plot of average chapter
Mastery Score of individual students against their
respective average test score. The graduate student
scores are plotted with ● and the undergraduate
student scores are plotted with *. The scatter plot
shows a positive relationship between the level of
mastery achieved and test score. Further, there is
a clear separation of average scores between the
graduate and undergraduate students.
Table 1. Chapters Common to OM and POM
Table 3. Average Mastery and Test Scores
Chapter
Description
Mastery Points
Graduate
Mastery Score
Undergraduate
Test Score
Productivity
98.46
94.44
7
Project Management
96.27
90.80
Managing Quality
6
Forecasting
91.89
93.13
1
Productivity
10
2
Project Management
10
3
Forecasting
4
Chapter
5
Statistical Process Control
3
Managing Quality
98.83
93.64
6
Inventory Management
7
Statistical Process Control
85.45
87.42
7
Aggregate Planning
7
Inventory Management
90.56
81.49
8
Materials Requirement Planning
8
Aggregate Planning
92.98
92.87
9
Scheduling
7
Materials Requirement Planning
94.08
87.17
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Table 4. Results of Overall Regression
Analysis of Variance
Source
DF
Sum of
Squares
Mean
Square
F
Value
Pr > F
174.81
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