I have problem set two questions, for my finance class
Please find attached the questions and the excle worksheet that you need to use for problem #3
FINA 3101, Fall 2020
Problem Set #3
Deadline: 5pm November 20, 2020
Directions: The problem set is due by 5 PM November 20, 2020. Problem sets must be submitted on
Blackboard by the deadline. Late assignments will absolutely not be accepted, but there is no penalty
to turning in an assignment early. Any students working together must turn in one set of solutions for
that group with all group members’ names on that copy. Group size is limited to no more than 3 students.
Using any computer file you did not create originally is expressly prohibited and will result in zero credit.
Googling for answers is expressly prohibited. All work submitted is required to be the original work
product of the group members. Failure to follow instructions will lead to a reduced grade.
On blackboard you will find the data file named FINA_3101_FALL_2020_PS3_data.xlsx containing data
you will need for this problem set.
Problem 1: The Single Index Model and APT [21 points]
See the worksheet named “Problem #1” for returns data that will help you complete this problem.
A. [6 points] Compute the excess return for two funds (BOTZ and VIG) and two stocks (MMM
and QCOM) and for the market by subtracting the risk-free return. Note that all returns provided
are monthly. Use those excess returns to estimate the market model (single index model) for each
of the 4 investments. Present the alpha, beta, and residual variance for each of the 4 investments.
B. [3 points] Using your estimate from part A, estimate the alpha and beta of the following
portfolio: 20% BOTZ, 40% VIG, 20% QCOM, and 20% MMM.
C. [4 points] Is the residual variance greater for the individual stocks (MMM, QCOM) or for the
funds (BOTZ and VIG)? Why do you think that is the case?
D. [4 points] Plot the 4 investments on the security market line.
E. [4 points] Estimate the Fama-French 3-factor model and report the coefficients, t-statistics,
and the R2.
Problem 2: Comparing performance [7 points]
Two investment professionals are comparing their return performance. The first professional managed
portfolios with an average return of 15% and the second professional managed portfolios with a 12% rate
of return. The beta of the first portfolio was 1.2 while the beta of the second was 1.0. The risk-free rate
of return was 2% and the expected market return is 10%.
A. [5 points] Which manager was a better selector of individual stocks, and why?
B. [2 points] Plot both of the portfolios on the security market line.
Problem 3: Closed End Funds [10 points]
See the worksheet named “Problem #3” for the prices and NAV of a recently IPO-ed closed end fund,
Angel Oak Financial Strats Income (FINS).
A. [5 points] For each day, compute the premium (discount) of the price to the NAV. Compute the
average daily premium.
B. [3 points] Plot the premium (discount) through time on a graph.
C. [2 points] Explain if this fund exhibits the same premium(discount) pattern around its IPO and
post-IPO period as the example we discussed in class.
Problem 4: Mutual Fund Returns [15 points]
You are considering investing in a mutual fund’s A share class that has a 2% sales charge (front-end
load) and annual expense ratio of 1.0%. Alternatively, you might invest in that fund’s C shares that
have no load and an annual expense ratio of 1.15%. Assume the fund’s assets return 10% annually.
a. [3 points] For a 2-year holding period, which share class will you prefer?
b. [3 points] For a 50-year holding period, which share class will you prefer?
c. [5 points] After how many years will the two mutual fund share classes result in the same
future wealth amounts?
d. [2 points] How does your answer to part c. change is the fund’s assets return 14% per
e. [2 points] What accounts for the greater expenses of the C-shares, compared to the Ashares?
JOURNAL OF EDUCATORS ONLINE
TEACHING QUANTITATIVE COURSES ONLINE:
ARE LEARNING TOOLS OFFERED BY
Mohammad Ahmadi, University of Tennessee-Chattanooga
Parthasarati Dileepan, University of Tennessee-Chattanooga
Kathleen Wheatley, University of Tennessee-Chattanooga
In recent years, online teaching has become extremely popular. Most institutions of higher learning
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%
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learning outcome of online education as good as
or better than face-to-face. However, an alarming
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along with historic trends, reveal a mismatch
between the growth in student demand for online
course offerings and the hesitancy of faculty to buy
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this mismatch is critical to realizing the full
potential of the online classes that the students are
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
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study that conducted one-on-one interviews with
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practices for effective online teaching: fostering
relationships, engagement, timeliness, communic-
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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
core educational needs and maintaining a sense of
teaching presence (Carliner & Shank, 2016). To
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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
on tests and the Mastery Points they earned
through the Quiz Me feature and found that there
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
for future research.
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
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viewpoints such as satisfaction, performance,
professor-student interaction, and a host of other
facets of teaching and learning. Smith and Bryant
case-based statistics classes and offer useful
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
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
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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
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university internet-based Introduction to Statistics
course and the psychopedagogical variables
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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
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found older/mature online instruction enrollees
performed better. Concerning satisfaction, a survey
of students of an online statistics course found
point Likert-scale (Al-Asfour, 2012). The study
instructions, communications, and assessments.
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online homework assignments, a study of an
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general, students preferred online homework
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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)
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as a supplementary tool in conducting assignment
and assessment in a mathematics course and found
of the MyMathLab platform.
Alrushiedat and Olfman (2013) conducted a
of asynchronous online discussions for business
statistics classes and found they facilitated more
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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
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
with the learning process, a greater understanding
of the material, as well as a reduction in the effort
further reported that the instructors experienced
a marked reduction in visits from students who
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covered material, a decrease in prep time during
online learning contexts.
model, described the outcomes of an interactive
team-teaching model while teaching an online
graduate-level disaster research and statistics
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responses to the team-teaching model and found
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synergy in content and pedagogies, continued
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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
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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
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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.
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course delivery method, online or face-to-face,
and gender affected academic progress. Through
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proportion of successful students in a course of
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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
in the 2010-2011 academic year. They reported
that the online and blended courses had a stronger
relationship with high course satisfaction than did
learning outcomes and the mode of course delivery.
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regression model to analyze a sample of 206 students
during the period from 2008 to 2012 and found that
age, major, degree obtained, and the number of
hours a student worked but not the choice of a more
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
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
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.
The opportunities thrown open by the
increasing popularity of online courses comes
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challenges such as mastering software platforms
for content delivery, interacting with students,
online content delivery, participation, assessment,
learning style, time management, and motivation.
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
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Publisher online platforms have modules that
provide the opportunity for students to practice
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.
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whether this process of earning Mastery Points
with unlimited trials of Practice and QuizMe
was helping student performance as measured by
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:
H0: The Mastery Score in a given chapter
does not have any effect on the test score in the
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HA: The higher the Mastery Score in a given
chapter the higher the test score will be in the
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.
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.
H0: There is no interaction effect between
course level and Mastery Score earned on the
average chapter test scores.
HA: There is an interaction effect between
course level and Mastery Score earned on the
average chapter test scores.
We chose Operations Management at the
undergraduate level and Production and Operations
Management at the graduate level. While there were
of topics between the undergraduate and graduate
common to both levels of classes. They are given
in Table 1.
Table 1. Chapters Common to OM and POM
Our study included 174 students over a period
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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
The summary of results is presented in Table
Mastery Score of individual students against their
respective average test score. The graduate student
student scores are plotted with *. The scatter plot
shows a positive relationship between the level of
a clear separation of average scores between the
graduate and undergraduate students.
Table 3. Average Mastery and Test Scores
Chapter Description Mastery Points
1 Productivity 10
2 Project Management 10
3 Forecasting 7
4 Managing Quality 6
5 Statistical Process Control 3
6 Inventory Management 7
7 Aggregate Planning 7
8 Materials Requirement Planning 8
9 Scheduling 7
Variable Description Variable
Course level Graduate or Undergraduate Course level
Chapter Assessment chapter Chapter
Mastery Score Percentage of subsections of
the chapter mastered
Mastery Time Time spent mastering the
Test Score Test score (0–100) Test Score
Variable Description Variable
Course level Graduate or Undergraduate Course level
Chapter Assessment chapter Chapter
Mastery Score Percentage of subsections of
the chapter mastered
Productivity 98.46 94.44
Project Management 96.27 90.80
Forecasting 91.89 93.13
Managing Quality 98.83 93.64
Statistical Process Control 85.45 87.42
Inventory Management 90.56 81.49
Aggregate Planning 92.98 92.87
Materials Requirement Planning 94.08 87.17
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relationship between the level of mastery achieved
and the test score as well as the separation between
graduate and undergraduate students is evident in
this plot as well.
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chapterwise test scores as the dependent variable
and Course level, Mastery Score, and Mastery
Time as the three independent variables. The
overall regression results are shown in Table 4, and
the results of individual and interaction effects are
shown in Table 5.
Table 4. Results of Overall Regression
Analysis of Variance
Source DF Sum of
Pr > F
Model 4 129783 32446 174.81 <.0001
Error 1427 264854 185.60
Corrected Total 1431 394637
Table 5.Results of Individual and Interaction Effects
The results in Table 4 show that the regression
between the chapter test scores as the dependent
variable and the three main effect variables and one
Table 5 shows some interesting results and based
on these results, three of the four hypotheses stated
earlier were rejected and one was not rejected.
These hypothesis test conclusions are discussed
Hypothesis 1: Since the p-value for the
main effect Mastery Score earned was less than
0.0001, the null hypothesis was rejected, and
we concluded the Mastery Score earned was a
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indicated an estimated increase of almost ½ point
for the chapter test score earned for every additional
Mastery Score earned.
Hypothesis 2: Since the p-value for the main
effect time spent earning the Mastery Scores
was 0.3687, the null hypothesis was not rejected.
Therefore, we concluded there was no evidence
found in the data that the time spent earning
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predicting the earned chapter test score.
Hypothesis 3: Since the p-value for the main
effect Course Level was less than 0.0001, the
null hypothesis was rejected, and we concluded
of the earned chapter test score. The estimated
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the graduate students on average were expected
Figure 1. Average scores of individual students
Figure 2. Chapterwise average score
Intercept 1 42.8999 1.75552 24.44 <.0001
Mastery Score 1 0.4456 0.02043 21.33 <.0001
Time spent earning
1 0.0021 0.00236 0.90 0.3687
Course level (Graduate) 1 25.8375 4.39389 5.88 <.0001
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to score a whopping 25 points more than the
Hypothesis 4: Since the p-value for the
interaction effect between Course Level Interaction
and Mastery Score earned was less than 0.0001, the
null hypothesis was rejected; and we concluded the
interaction between the course level and Mastery
test score earned. The estimated regression
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from earning more Mastery Scores than the
The process of earning Mastery Score resulted
in students spending more time with practice
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a better understanding of concepts tested in the
chapter tests, and, therefore, scored higher. This
result reveals a potential predictor of performance
in addition to GPA, as found by Dotterweich and
The result of the second hypothesis, namely
the time spent earning the Mastery Scores, was
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However, the time recorded by the MyOMLab
system is the duration of time the students were
connected to the online tool, which may not be the
same as the time actually spent working on earning
Mastery Scores. It may have included idle time
when students took a break or failed to log off after
completing the task. Therefore, it was very likely
that the time recorded by the system was not the
accurate measure of time students actually spent
working on earning the Mastery Scores, and this
inaccuracy may have contributed to the conclusion
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chapter test score earned. In any case, even if the
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in determining the chapter test score earned, the
conclusion of Hypothesis 1 showed that earning
While the conclusion of Hypothesis 3, which
showed that graduate students scored higher on
chapter tests than undergraduate students, was
not surprising, the magnitude of the difference
was. The level of commitment and dedication of
a graduate student were likely reasons for this.
Another possible reason for this disparity was that
suitable for graduate students than undergraduate
The conclusion of Hypothesis 4 revealed an
for the interaction term between course level (1
= Graduate, 0 = Undergraduate) and Mastery
Score earned showed that the higher the Mastery
Score the undergraduates had, the higher their test
scores were relative to the graduate students (all
other factors held constant). If one of the reasons
we speculated for the conclusion of Hypothesis 3,
were less suitable for undergraduate students than
graduate students was true, then one possible
mitigation strategy was to encourage students to
earn more Mastery Scores. This may be achieved
by setting a higher standard for earning the Mastery
Score than the 80% we used or by using higher
Over the last decade, online course delivery has
seen a remarkable growth. Studies show that the
demand for online offerings will continue to grow.
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to students, instructors are hesitant to embrace
them fully. One of the factors for this hesitancy
is the uncertainty with respect to which strategies
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students and help them to succeed in mastering
course objectives. This study demonstrated the
practicality of tools such as Practice and Quiz Me
of the Pearson MyLab platform. In addition, such
publisher-developed online tools can help bridge
the gap in performance between online and faceto-face undergraduate economics courses in the
United States that Sohn and Romal (2015) found.
We found that the time spent earning the Mastery
improve their chapter test scores; however, it was
very likely that MyOmLab overstated the time
spent by students earning the Mastery Scores. The
time reported by MyOmLab was the duration of
time a student was connected to the system, which
may have been longer than the actual time spent
working because students may take breaks or, in
some instances, students may not terminate the
session after completing their work. Therefore,
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we cannot be certain that time spent was not a
attempting the corresponding chapter test, though
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to earn the Mastery Score before attempting the
effect on student performance in chapter tests is a
topic for future research.
JOURNAL OF EDUCATORS ONLINE
Al-Asfour, A. (2012). Examining student satisfaction of online
statistics courses. Journal of College Teaching & Learning
(Online), 9(1), 33–38.
Allen, I. E., & Seaman, J. (2016). Online Report Card: Tracking
Online Education in the United States. Babson Park, MA:
Babson Survey Research Group.
Alrushiedat, N., & Olfman, L. (2013). Aiding participation and
engagement in a blended learning environment. Journal of
Information Systems Education, 24(2), 133–145.
Bailey, C. J., & Card, K. A. (2009). Effective pedagogical practices
for online teaching: Perception of experienced instructors.
The Internet and Higher Education, 12(3-4), 152–155.
online on student achievement in online economics and
Carliner, S., & Shank, P. (Eds.). (2016). The e-learning handbook:
past promises, present challenges. New York, NY: John Wiley
Cavanaugh, J. K., & Jacquemin, S. J. (2015). A large sample
comparison of grade based student learning outcomes in
online vs. face-to-face courses. Online Learning, 19(2), n2.
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and multiple measures of learning outcomes. Journal of
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television and traditional delivery: Student characteristics and
success factors in business statistics. American Journal of
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of Information and Communication Technology Education
(IJICTE), 7(4), 72–83. doi:10.4018/jicte.2011100107
Hegeman, J. S. (2015). Using instructor-generated video lectures
in online mathematics courses improves student learning.
Online Learning, 19(3), 70–87.
Katz, Y. J., & Yablon, Y. B. (2003). Online university
learning: cognitive and affective perspectives.
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WHY DO LEARNERS CHOOSE ONLINE LEARNING:
7HE LEA5NE56¶ 92ICE6
Hale Ilgaz and Yasemin Gulbahar
Ankara University, Distance Education Center, 06830 Golbasi, Ankara, Turkey
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.
Online learning, preferences, expectations
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, ³In fact,
there has never been a better time to be in the field of open, flexible, distance and online education than
QRZ!´ (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 GRQ¶W benefit from
these advantages of technology usage and its support in teaching-learning processes.
A variety of reasons might account for these learning preferences. da÷OaU aQG TXUJXW (2014) attempted to
identify the effective factors for the e-learning preferences of university students; they concluded that,
³EIILcLHQW XVaJH RI WLPH aQG UHGXcHG HGXcaWLRQaO H[SHQVHV ZHUH IRXQG WR bH RQ WRS RI WKH OLVW aV WKH PRVW
valued advantages of e-OHaUQLQJ´ (S. 46). Moreover, having responsibilities, a full-time job and no access to a
nearby university may also cause learners to prefer online learning.
APRQJ WKH IacWRUV WKaW aIIHcW OHaUQHUV¶ aWWLWXGHV WRZaUG H-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 VWaWHG WKaW ³« 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´ (S. 282).
RHJaUGOHVV RI OHaUQHUV¶ aWWLWXGHV WRZaUG H-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 cROOHcWLQJ OHaUQHUV¶ JHQHUaO
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
1. What are the factors that affect VWXGHQWV¶ SUHIHUHQcHV IRU RQOLQH OHaUQLQJ?
2. Are there any differences between program types in terms of student preferences?
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.
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
f % f %
Gender Female 1278 59,92 184 55,93
Male 855 40,08 145 44,07
Marital Status Single 1032 48,38 133 40,43
Married 1101 51,62 196 59,57
18-25 29 9 860 41
26-33 136 41 761 36
34-41 112 34 398 19
42-49 45 14 80 4
50 and up 7 2 18 1
Total 2133 100 329 100
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 WKH SaUWLcLSaQWV¶ cRPPHQWV, aQG 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, CRKHQ¶V KaSSa cRHIILcLHQW ZaV caOcXOaWHG
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.
3.1 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 f %
Having a full time job 441 38,31
Accessibility and flexibility 218 18,94
Individual responsibility 113 9,82
Effective time management 106 9,21
Individual difficulties 83 7,21
Features of learning environment 82 7,12
Physical distance 43 3,74
Academic preference 23 2,00
Having a second degree 16 1,39
Institutional prestige 10 0,87
Aging 8 0,70
Disability 8 0,70
Total 1151 100
According to the data analysis, having a full time job is the most significant theme regarding the VWXGHQW¶V
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:
³I am Zorking, and m\ age is 35. Still, I can complete m\ education into m\ area of
interest, and have a diploma via distance education.´ [P-722]. ³I 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].
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 LQ WHUPV RI VWXGHQWV¶ SUHIHUHQcHV.
³Distance 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]. ³It¶s ver\ eas\ 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]. ³I choose distance education, because I can stud\
whenever I want. I can listen to session recordings and there isn¶t an obligation about
attending s\nchroni]ed sessions.´ [P-373].
Another characteristic of distance education students is that, generally, WKH\ cRXOGQ¶W cRPSOHWH, 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.
³I had to choose distance education, because there is no one to take care of m\ nepheZ.´
[P-53]. ³I am married, and have 3 kids. I really appreciate that this opportunity is provided
to us.´ [P-491]. ³I choose distance education because I am married and have 2 kids. M\
kids are going to elementar\ school, so the\ 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 VWXGHQWV¶ GaWa, UHVHaUcKHUV IRXQG WKaW 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:
³I have a physical disability; as a result of this, transportation is a problem for me. So, I
choose distance education´ [P-522]. ³I am a congenitall\ hearing disabled person; with
distance education I can listen to m\ courses over and over´ [P-840]. ³The cit\ I lived in
doesn¶t have m\ program¶s formal version´ [P-121]. ³I am travelling a lot because of m\
job, so I have to be in different cities most of the time´ [P-327]. ³The appealing factor for
me Zas the universit\¶s prestige. Having a diploma from such big university is very
important for me´ [P-878]. ³I 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].
3.2 Graduate Students
After analyzing the JUaGXaWH VWXGHQWV¶ GaWa, 8 WKHPHV arose. Compared with the uQGHU JUaGXaWH VWXGHQWV¶
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 90 44,12
Effective time management 42 20,59
Accessibility and flexibility 26 12,75
Lifelong learning 24 11,76
Physical distance 13 6,37
Individual responsibility 7 3,43
Institutional prestige 1 0,49
Disability 1 0,49
Total 204 100
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:
³Distance education provides me with continuing education, and I¶m improving myself
academically as well as in my work life´ [P-13]. ³I believe in lifelong learning, but I am
dealing Zith a high tempo Zork life. I couldn¶t attend a traditional program because of m\
workload, so I choose distance education. Distance education is a very useful system for
busy people like me´ [P-46]. ³I choose distance education because it Zas the most
appropriate method with which I can continue with minimum loss elsewhere. Besides, I
believe that, after completing this program, I Zill be in a better position in m\ Zork life´
When looking over the order of the themes, having a full time job was the most important, as was the case
in the XQGHUJUaGXaWH SURJUaP VWXGHQWV¶ GaWa. 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.
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
IURQW RI SHRSOH¶V choices. In this study, the researchers aimed that identify WKH GLIIHUHQcHV bHWZHHQ VWXGHQWV¶
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 studentV¶ 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. SLPLOaU GLIILcXOWLHV aUHQ¶W RbVHUYHG
at graduate level. Graduate level is not an obligation for a job; it depends much more on intrinsic motivation.
This is why these seven WKHPHV ZHUHQ¶W HYLGHQW LQ WKH 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¶W cRPSOHWH RU HYHQ VWaUW WKHLU
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¶V lives. With
the use of regular tracking systems, educational practitioners can better understand VWXGHQWV¶ UHaVRQV for
preferring distance learning, as well as their expectations. Thus, institutions can provide a more enhanced and
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Fall2020 TSM320 Final test
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