Manajemen | Fakultas Ekonomi Universitas Maritim Raja Ali Haji joeb.79.4.237-244
Journal of Education for Business
ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Teaching MBA Statistics Online: A Pedagogically
Sound Process Approach
John R. Grandzol
To cite this article: John R. Grandzol (2004) Teaching MBA Statistics Online: A Pedagogically
Sound Process Approach, Journal of Education for Business, 79:4, 237-244, DOI: 10.3200/
JOEB.79.4.237-244
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Teaching MBA Statistics Online:
A Pedagogically Sound
Process Approach
JOHN R. GRANDZOL
Bloomsburg University of Pennsylvania
Bloomsburg, Pennsylvania
D
istance education—from traditional correspondence courses conducted on an individual basis via regular
mail to cohort-based, Web-delivered
courses that incorporate synchronous
video—is proliferating globally in private and public sectors (Steiner, 1998).
Organizations (academic and otherwise)
aggressively market undergraduate- and
graduate-level courses and programs,
leaving student consumers at risk concerning the cost, quality, and usefulness
of their education.
Distance education has several defining elements (Steiner, 1995): the separation of instructor and learner during the
majority of the instructional process, the
use of educational media to unite the
teacher and learner and to carry course
content, and two-way communication
between the instructor and learner.
These elements are not, however, defining in terms of operational definition,
which makes clear a methodology for
delivering quality education at a distance. Distance education methods can
and do vary substantially.
The lack of empirically validated best
practices for distance education contributes to hesitation in using this education delivery medium. Additionally,
questions about identifying target audiences—how to find the individuals or
groups of individuals best suited to
ABSTRACT. Delivering MBA statistics in the online environment presents significant challenges to education and students alike because of
varying student preparedness levels,
complexity of content, difficulty in
assessing learnng outcomes, and faculty availability and technological
expertise. In this article, the author
suggests a process model that develops key learning strategies for the
online environment and offers accreditation- and literature-based guidelines for overcoming obstacles to
sucessful mastery of MBA-level statistics. Intended for faculty members,
administrators, and students with limited experience in online education,
the model may stimulate discussion
and investigation of successful practices appropriate for teaching business statistics.
become distance learners—make the
whole issue of distance education rather
intimidating. Regardless, the “train has
left the station,” and not boarding it
proactively and knowledgeably seems
an inappropriate course of action.
Growth of Distance Education
Programs and Courses
The Council for Higher Education
Accreditation (2002a) estimated collegiate enrollment in distance education
at 2.2 million students in 2002. Thirtyfive of the 50 states in the United States
already have statewide virtual universities. Of 5,655 accredited academic
institutions, 35% offered some form of
distance learning programs or courses.
Of these, 86% held regional accreditation. These data attest to the regional
accrediting bodies’ leadership in recognizing the legitimacy of distance education as a viable alternative to traditional on-campus program and curriculum
delivery (Council for Higher Education
Accreditation, 2002b).
Smith (2001) listed many of the benefits that distance education provides
for both students and faculty members.
For students, these include accessibility,
flexibility, participation, absence of
labeling, written communication experience, and experience with technology.
Faculty members enjoy the same benefits and perhaps employment advantages derived from newly gained skills.
There are difficulties as well, notably
issues concerning interaction and team
building, administration of examinations, absence of oral presentation
opportunities, and technical problems.
From the faculty perspective, the time
requirement is significant, and activities
include designing courses, learning new
technologies, and resolving technological problems. All these factors contribute to the strong support for and
rapid development of distance education in many settings, as well as the
indifference or hesitation in others.
March/April 2004
237
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Best Practices
Academic quality, driven by the successful enabling of learning, requires
interaction among faculty members and
students. Shea, Motiwalla, and Lewis
(2001) reported that top students,
although generally satisfied with the
distance education environments, desired more frequent and faster interaction (meaning feedback) with professors. The Council for Higher Education
Accreditation (2002b) suggested that
programs should “develop a sense of
community through study groups”
(p. 12). The eight regional accrediting
commissions reinforced this view,
emphasizing the “community of learning” perspective that suggests that faculty members be involved cooperatively
in course creation and delivery and that
learning be dynamic and interactive,
regardless of the setting (Middle States
Commission on Higher Education,
2002). In its study of benchmarks for
success in distance education, the Institute for Higher Education Policy (2000)
qualified interactivity as the “sine qua
non for quality” (p. 16).
Larson (2002) argued that whereas
traditional education is centered on the
professor, distance education is inherently student centered. This suggestion
has a rather profound implication:
Although the instructor may continue to
orchestrate the course, the students
actually may direct the learning process,
relying on their own initiative and
frames of reference. Recognizing this
perspective suggests questions about
how to differentiate these roles. For
example, what boundaries should the
course establish, and how much flexibility should it allow? This paradigm
requires faculty members to mentor,
facilitate, and enable while ensuring
mastery of fundamental and rigorous
statistical concepts and methods (in this
case) for which creativity and innovation may be problematic.
In the traditional setting, students discuss and exchange ideas about course
topics in and out of the classroom. Faculty members are involved in many of
these occasions, and they add lecture
interaction and office hours. Boose
(2001) suggested that professors should
first ask themselves how they can repro238
Journal of Education for Business
duce the classroom experience or design
a course structure that accomplishes
learning objectives differently. In an
online
environment,
replicating
exchange opportunities requires planning, monitoring, and participating in
all such opportunities to ensure knowledge is being exchanged and understood. Faculty members cannot simply
observe, as in a classroom.
The most difficult portion of the traditional environment to re-create is
interactive lecture. Although an instructor may rely on written notes to ensure
logical flow of presentation and discussion, much of the spoken word in a
classroom is not scripted, frequently
because it is spontaneous. Yet without
the unscripted dialogue, learning may
be incomplete; hence, special attention
must be afforded its proxy. The lecture
material links the textbook and related
readings and facilitates student participation in all available media. Whatever
the number and extent of activities
designed to replace classroom lectures,
online course professors must ensure
their consistency, comprehensiveness,
and clarity.
In its comprehensive review of the literature and a survey of faculty members, students, and administrators at
institutions recognized for their extensive experience and high-quality distance learning programs, the Institute
for Higher Education Policy (2000)
identified and then confirmed distance
learning program benchmarks in seven
categories: institutional support, course
development, teaching/learning, course
structure, student support, faculty support, and evaluation and assessment.
Course developers should use desired
learning outcomes to determine the
technology to be used, and courses
should be designed to engage students
in the practice of desired competencies.
Effective distance learning requires
facilitation of student interaction, including timely feedback for questions
and assignments and instruction in
effective research methodologies.
Course structure should ensure that students receive adequate information
about the distance learning aspects of
the program or course. This should
include supplemental information about
objectives, concepts, ideas, and out-
comes, as well as expectations concerning timely submission of assignments
and reasonable timeframes for grading
and feedback. Student support should
include hands-on training on courseware, library access, course software,
and the like. For the area of evaluation
and assessment, findings suggest application of specific standards measured
via several assessment methods.
The School and the Course
Bloomsburg University, located in
central Pennsylvania, is one of 14 universities in the state’s system of higher
education; it serves a primarily undergraduate population of approximately
7,500 students drawn from neighboring
communities and small cities. The MBA
program has an enrollment of approximately 100 students who have the option
of completing a “generic” MBA or
choosing a concentration. Statistical
Analysis and Design is a foundation
course in the program typically taken by
MBA students. Before summer 2002,
none of the MBA courses at Bloomsburg
were offered via distance education.
Environmental Context
The impetus for developing an online
version of this particular course derived
from three distinct sources. Studies
(Eaton, 2001; IHEP, 2000) suggested
that individual faculty members seeking
personal development in technological
learning enhancements or seeking to
satisfy innate desires for new knowledge initiate online courses and even
development of entire online programs.
A second reason for considering online
course development at Bloomsburg was
a predicament resulting from traditional
delivery of the MBA at a location 50
miles from the main campus. Having
made the commitment to allow potential
students to complete MBA degree
requirements within a definite timeframe and without requiring attendance
at the main campus, the participating
departments’ faculty resources were
being stretched to the limit as they
struggled to ensure that all courses were
taught by instructors with doctoral
degrees. Finally, in an effort to increase
the growth of the program and decrease
the time required to complete it, program administrators determined to offer
some or all of the foundation courses
during summer sessions. Because very
few of Bloomsburg’s MBA students are
full-time, offering students a way to
meet family and work commitments in
addition to completing course work
seemed reasonable. Making foundations
courses more accessible (via distance
education) enables new students to
enroll in core courses in their first fall
semester, thereby speeding the time to
degree completion.
• Learn and understand the purpose,
value, and methods of descriptive
statistics
• Learn and understand key concepts
of probability theory
• Learn and understand the purpose,
value, and methods of inferential
statistics
• Transform data into management
information using probability and
statistics
These objectives become the foundation
of assessing learning outcomes and
drive the development of the appropriate online capabilities.
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Statistical Analysis and Design
My pedagogical approach to the business curriculum concurs with Markel’s
(1999) view that pedagogy is not
instructional-mode dependent. My
approach, interpreted specifically for an
MBA statistics class, can be stated as
follows: The class should impart a solid
foundation of knowledge and theory
and a thorough understanding of statistical methodologies through extensive
demonstration of applications to forprofit and not-for-profit organizations.
By integrating research, writing, presentation, and computer skills, along with
case analyses and technical methodologies in the learning experience, students
successfully progress from learning why
certain knowledge is necessary and
important, through learning what the
knowledge actually includes, to learning how to use and apply it. Ideally,
graduates will have a general knowledge of business statistics and will
know more than what to do and how to
do it—they will know why they are
doing it.
This pedagogy drives the development of the course objectives and content, as well as its structure and assessment techniques.
Content
The content for this course consists of
typical topics ranging from descriptive
statistics to multiple regression. Although the literature suggests that some
of these should be replaced by more
applied topics, such as quality control or
time series, this issue is not a key source
of discontent in Bloomsburg’s program
because these additional topics are covered elsewhere in the program.
Expectations
A recurring theme in the research literature and accrediting guidelines is the
need to make clear to students what is
expected, not only in terms of output
(homework, papers, etc.) but also in
terms of the level of knowledge and
ability that they should achieve. Phipps,
Wellman, and Merisotis (1998) suggested clearly identifying a set of outcomes
in terms of expected student knowledge,
skills, and competency levels. Advising
students about the program course, its
objectives, concepts, ideas, and outcomes, is a key practice among the most
successful practitioners of distance education (IHEP, 2000).
Transitioning the Course
In Table 1, I list the principal opportunities for interaction in the traditional
learning environment and propose corresponding alternatives in the computermediated (Internet) learning environment. These opportunities cover formal
and informal professor-to-student exchanges as well as corresponding student-to-student opportunities. Assuming
acceptance of the preceding discussion
on the importance of interactivity in the
learning process, a key challenge for faculty members teaching in online courses
is the transformation of traditional
opportunities by effective use of online
capabilities. Experience suggests that
this is no easy task; a semester of seemingly innumerable e-mail messages
made this quite evident to me.
Care in transforming the standard traditional business statistics class is
essential. Otherwise, existing students
could be alienated, with a potential negative impact on planned program
growth. Despite the availability of distance learning course software within
the state system without any explicit
cost to individual member universities,
there had been very little activity at this
campus in this arena; hence, a gradual
phase-in, from the Web-enhanced
(blended) course to the Web-based
(online) course, was chosen as the
appropriate strategy. I used this
approach by adding Web enhancements
to all of the courses that I taught, both
graduate and undergraduate, on and off
TABLE 1. Interactions in Traditional and Online Learning Environments
Behavioral Objectives
Traditional
Each course in the College of Business has behavioral objectives—in other
words, what students are expected to do
with the knowledge gained in the course.
The objectives that follow represent the
knowledge, skills, and abilities that students are expected to achieve in this
course regardless of the delivery media:
Lectures
Assignments
Question and answer
Formal discussions
Informal discussions
Office visits
Phone calls
E-mail messages
Online
Learning units
File exchange
Discussion forums, chat rooms
Discussion forums, chat rooms
Chat rooms, group pages
Chat rooms
Phone calls
E-mail messages
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239
TABLE 2. Course Web Site Structure
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Course information
Syllabus
Calendar
File names
Word file format
Excel file format
Online chats
Practice quizzes
Midterm examination
Final examination
Faculty and course
evaluation
Faculty information
Introduction
campus. Students began to appreciate
the learning or improving of skills related to time management, technology, and
electronic communication.
Initially, only distribution of documents—syllabi, course outlines and
schedules, assignment guidelines and
formats, and any supplemental reading
material or information—was handled
via the course Web site. Next, I added
announcements dealing with course
requirements, learning opportunities,
assignment submissions, and examinations. After these, I added discussion
forums for questions about homework
and other class activities, and finally, I
created examinations for online applications and scheduled them in on-campus
computer labs.
Realizing that student feedback was
essential to additional support for further
distance learning activities, I developed
a multiquestion supplement to the
approved student evaluation administered each semester. My questions
addressed students’ use and opinions of
the online courseware and course-related
software. Results showed significant
interest in and appreciation of the Webbased enhancements. Having supporting data before I initiated the online
courses proved a significant benefit,
because the contractual environment at
Bloomsburg commands supplemental
payments for online course development and teaching. Budgets are usually
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Journal of Education for Business
Assignments
Homework
assignments
Homework
exercises
Best of class
Research paper
Course documents
Introduction
Learning objectives
Tasks
Descriptive statistics
Learning objectives
Visual statistics
Tasks
Probability
Discrete random variables
Continuous random variables
Sampling distributions
Confidence intervals
Hypothesis testing
Two-sample statistical inference
Experimental design and
analysis of variance
Simple linear regression analysis
Multiple regression analysis
an issue, and fortunately, opinions were
not the deciding factors.
Online Version
The online version of Statistical
Analysis and Design at Bloomsburg
University does not have a unique
course description. An advertisement
describing the mechanics of the course,
the time frame in which it would occur,
and the faculty member overseeing its
development and delivery was made
available to all current graduate students
and all new program applicants.
Upon registration, students received
information about how the course would
be conducted; they were also advised of
required attendance at two on-campus
sessions. During the first session, held in
a computer lab at the beginning of the
course, students were walked through the
course software (in this case, Blackboard). They enrolled themselves in the
course Web site and practiced with all of
the courseware features that would be
used: announcements, discussion forums,
file exchanges, virtual classrooms, and
other student tools. These initial sessions
ensured that all students had at least a
basic level of familiarity with and competency in courseware functions. The second session consisted of an in-lab software demonstration that was scheduled
to coincide with the teaching of relevant
course content.
Books
Business Statistics
in Practice
Other readings of
interest
The course Web site was organized
through Blackboard’s existing framework. In Table 2, I depict this structure.
Note the detail provided in the course
information section, the variety of
interaction opportunities listed under
“Communication,” and the information
provided concerning assignments, all
of which were intended to facilitate the
community of learning mentioned
previously.
To maintain timely, sequential, and
cumulative learning of the course’s content, I made material available on the
various Web pages 1 week before it was
listed in the course schedule. This prevented students from getting too far
ahead of their peers or simply attempting to complete all of the graded work
in a very short time period, forfeiting
the benefit of student and faculty interactions. For this same reason, all assignments had due dates that could not be
exceeded without penalty. Students did
have the option to submit their work
earlier if personal or work conflicts precluded them from submitting on the
actual due dates. Initial submissions for
discussion forums had mandatory posting dates, which helped alleviate the
common difficulty of later postings’
sounding too much like earlier ones.
Additionally, all students were required
to attend at least 1 hour of online chat
(virtual classroom) per week, during
which they were to present problem
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Web resources
Communication
Research Web resources
Send e-mail
Problem solving and
Discussion board
statistics Web resources
Virtual classroom
Roster
Group pages
solutions, answer the instructor’s questions, contribute to discussions, and ask
any unresolved questions.
The issues of originality, interactivity, and applicability are important in all
courses. In the online environment,
these become even more important, as
students do not have the opportunity
for simultaneous discussion and observation. For example, in a traditional
classroom, the instructor can draw a
graph and respond to comments or
questions as the picture unfolds.
Although this is possible during a virtual classroom, time constraints usually limit these opportunities. For this
reason, along with a pedagogy that
encourages providing both analytical
and visual explanations of key concepts, techniques, and methodologies,
choosing a textbook that included an
Excel add-in (with tutorials in chapter
appendixes) and supplementary software that provided graphic depictions
of all covered statistical concepts
proved critical. Another useful technique was the weekly requirement that
students either discuss, in jargon-free
language, a particular concept or analytical result or actually conduct an
experiment using a statistical technique
(e.g., ANOVA or regression) and prepare a report for the discussion forum
for all other students to see and discuss. The following is an example of
this requirement:
Student tools
Digital drop box
Edit your homepage
Personal information
Calendar
Check grade
Manual
Tasks
Electronic Blackboard
Address book
After thoroughly reviewing Section 7.3,
Sample Size Determination, create a business scenario that requires determination
of a sample size for estimating a particular population mean. Refer to the formula
on page 257 of the text, and read the paragraph directly under it. Choose a reasonable value for B (error bound), and follow
the second alternative for estimating the
population standard deviation described
on page 259. Then compute sample sizes
for both a 95% and a 99% level of confidence. To respond successfully, you must
provide a clear and concise narrative
describing this particular business scenario, including the characteristic of
interest, the unit of measurement, justification for the error bound value chosen,
calculation of the standard deviation estimate, the sample size results, and onesentence “translations” for each.
Note that this example is perfectly suitable and appropriate, yet optional, for a
traditional MBA statistics class. In the
online environment, the option becomes
a necessity.
In addition to the weekly discussion
forums, students participated in a variety
of assignments that gave them opportunities to learn via their own information
filtering system—visual, auditory, or
kinesthetic (Torres, 1985). They solved
homework problems, responded to
inquiries from the instructor in online
chat rooms, prepared case analyses and
received both individual and summary
feedback, researched and prepared a
report on the misuse of statistics in the
media, completed instructor-prepared
tutorials in course-related software,
reviewed outstanding work from other
students, initiated group discussions or
online chats, and read textbooks, supplemental articles, instructor notes,
PowerPoint presentations, and other
Web sites. Each content lesson contained a list of tasks detailing pertinent
student activities.
Assessing Learning Outcomes
Use of multiple assessment techniques is necessary to derive reliable
results specific to individual students.
My pedagogy incorporates this perspective regardless of environment. The
intent is to enable students to succeed
and to provide ample opportunity for
them to demonstrate their understanding of and ability to apply the course
content. Measuring how well students
achieve the behavioral objectives of an
MBA statistics course requires frequent
and varied assessments of direct applications of statistical methodology and
techniques to practical business problems, issues, and opportunities.
Compared with the traditional version of the course, the online version
has nearly identical assessments; however, their frequency and intensity are
greater. For example, although both
delivery formats require that an Excel
spreadsheet solution to a homework
exercise contain adequate explanations
for all methods and results, the two formats handle discussions in very different ways. In the traditional classroom,
instructors observe and listen to students’ comments and perceptions. In the
online environment, comments must be
written, which usually requires more
effort from both the students and
instructor. An advantage of the virtual
classroom feature is the automatic
archiving of all communications. Faculty members and students can access
transcripts of past chats to determine
levels of participation and accuracy or
to review guidance and explanations.
This is similar to automatic note taking
and attendance recording.
A comparison of examination results
from online and traditional sections of
this course (run in nonconsecutive
semesters) shows conflicting results
from this assessment measure. Applying
March/April 2004
241
242
Journal of Education for Business
Traditional
Online
50
25
Sig
nif
ica
nc
e
Ind
ep
en
de
nc
e
En
co
ura
ge
me
nt
Cla
rity
Ev
alu
ati
on
Gr
ad
ing
0
Pre
pa
red
Percent Responding in Highest Categories
75
En
thu
sia
sm
FIGURE 1. Student evaluations: Comparison of traditional versus online
instructor-based traits.
100
Traditional
Online
75
50
25
Co
mm
un
ica
tio
n
Un
de
rst
an
din
g
Ab
ility
0
Kn
ow
led
ge
Several studies (Council for Higher
Education, 2002a; IHEP, 2000; Phipps
& Merisotis, 1999) suggested the
importance of measuring overall student
satisfaction with the distance learning
experience in addition to assessing
learning outcomes. Students in both the
traditional and online course offerings
completed identical versions of
Bloomsburg’s standard faculty and
course evaluation. All students also
completed a supplemental survey
addressing the Web-based and software
features available within the course
structures.
In Figure 1, I show comparative
results for these identical surveys from
the same two course sections used for
the comparisons of test scores. These
results for the questions pertaining to
instructor-based traits are virtually identical. Small enrollments precluded z
tests of these respective proportions.
In Figure 2, I show results typically
associated with the learning that occurs,
including questions about students’ selfperceptions of knowledge, ability,
understanding, comprehension, and
communication. With the exception of
communication skills, results were
again similar, with differences due to
responses from one or two students (of
the 11 or 12 in these sections). Note also
that the results do not include students
“in the middle,” but only those responding in the two most positive response
categories (on a five-point scale).
Finally, the data in Figure 3 suggest
several interesting and beneficial advantages based on students’ perceptions
about various course tools, corollary
competencies, and personal learning
100
Co
mp
reh
en
sio
n
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Student Satisfaction
Percent Responding in Highest Categories
the small sample t test for the means
suggests no significant difference
(H0: µ1 – µ2 = 0) between these two
groups of students in scores from identical midterm examinations (p value =
.26), whereas the same hypothesis can
be rejected (p value = .02) for the final
examination. Like so many other empirical reports (Russell, 2002), these results
are inconclusive because of their conflicting implications. Also, relying solely on test scores is a recognized weakness of learning outcomes assessment.
FIGURE 2. Student evaluations: Comparison of traditional versus online
learning perceptions.
strategies. The response scale for these
items did not include a middle ground
(the scale included strongly agree,
somewhat agree, somewhat disagree,
and strongly disagree). In fact, the only
item on which online students expressed
lesser preferences than traditional stu-
dents pertained to Blackboard-delivered
course examinations. This lower satisfaction correlates with the inconclusive
(weak) results of statistical tests discussed previously. Otherwise, they rated
the online experience higher than its traditional counterparts on improvement of
Traditional
Online
50
25
0
of
Inf
orm
ati
on
Te
Ex
ch
am
no
log
ina
yC
tio
ns
om
mu
nic
ati
on
Sk
ills
Co
mp
reh
en
sio
So
n
ftw
are
Pre
fer
en
ce
So
ftw
are
Te
Im
ch
pa
no
ct
log
yD
ec
isio
nS
kill
s
Percent Responding in Highest Categories
75
Dis
se
mi
na
tio
n
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100
FIGURE 3. Student evaluations: Comparison of traditional versus online
course tools and corollary skills.
al (regional) and specific (AACSB) levels, which include the need for interactivity among students and faculty members, is an appropriate strategy. My
original intent—to create at least as
many opportunities for the exchange of
information (see Table 1) in the online
environment as there are in the traditional classroom—appears viable in terms of
enabling student learning. Additionally,
the improvement in corollary skills (e.g.,
communication and technology) may be
substantial and suggests a valuable
advantage for successful online students.
Quantitative methods for assessment
of differences in learning outcomes were
limited in this application. Test scores
are only one of the many assessment
techniques typically employed, and student evaluations vary from institution to
institution. Qualitative techniques for
assessing differences may provide additional insights to true learning outcomes
and supplement the numerous “no significant difference” findings already
published in the literature.
REFERENCES
their technology-based communication
and decision-making skills and on the
value of the accompanying application
software. Results were nearly identical
for items pertaining to the value of the
Web site for disseminating information
and its contribution to learning course
content. Note that the traditional course
from which these data derive did incorporate the Blackboard platform and use
of basic Web capabilities in addition to
the weekly class meetings.
Conclusion
The benchmarks established in the
IHEP (2000) study were applied in the
successful transition of this course to
the distance format. Desired outcomes,
stated as behavioral objectives in the
course syllabus, directed the course
structure to levels of technological competence that all students could be
expected to have. The activities for students, the software tutorials, required
research, self-designed experiments,
discussion forums, homework exercises, and examinations, motivated (in fact,
required) students to become engaged
fully in the application of statistical
methods to business contexts. Students
knew expectations in terms of the volume and level of work, as well as the
time constraints and submission
requirements. The instructor remained
cognizant of and responsive to timely
(i.e., rapid) and constructive feedback.
Students received early and repeated
information about the course, how it
worked, and the different activities that
they would experience. Results were
assessed through a variety of techniques, which allowed every student to
demonstrate his or her grasp of the
material in the way that enabled each
student best.
This case review suggests that pedagogy is not necessarily a function of
delivery environment. This transition of
an MBA statistics course to an online
format was driven by professional development, cost, and student convenience factors, without a strategic initiative to create an online program of study,
and the results of this study show that
pedagogy was not compromised. Creating the course structure based on accreditation standards and ideals at the gener-
Boose, M. A. (2001). Web-based instruction: Successful preparation for course transformation.
Journal of Applied Business Research, 17(4),
69–79.
Council for Higher Education Accreditation
(CHEA). (2002a). Distance learning in higher
education. CHEA update (2). An ongoing study
on distance learning in higher education prepared for CHEA by the Institute for Higher Education Policy (June 1999). Retrieved November
13, 2002, from http://www.chea.org/ Research/
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ISSN: 0883-2323 (Print) 1940-3356 (Online) Journal homepage: http://www.tandfonline.com/loi/vjeb20
Teaching MBA Statistics Online: A Pedagogically
Sound Process Approach
John R. Grandzol
To cite this article: John R. Grandzol (2004) Teaching MBA Statistics Online: A Pedagogically
Sound Process Approach, Journal of Education for Business, 79:4, 237-244, DOI: 10.3200/
JOEB.79.4.237-244
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Teaching MBA Statistics Online:
A Pedagogically Sound
Process Approach
JOHN R. GRANDZOL
Bloomsburg University of Pennsylvania
Bloomsburg, Pennsylvania
D
istance education—from traditional correspondence courses conducted on an individual basis via regular
mail to cohort-based, Web-delivered
courses that incorporate synchronous
video—is proliferating globally in private and public sectors (Steiner, 1998).
Organizations (academic and otherwise)
aggressively market undergraduate- and
graduate-level courses and programs,
leaving student consumers at risk concerning the cost, quality, and usefulness
of their education.
Distance education has several defining elements (Steiner, 1995): the separation of instructor and learner during the
majority of the instructional process, the
use of educational media to unite the
teacher and learner and to carry course
content, and two-way communication
between the instructor and learner.
These elements are not, however, defining in terms of operational definition,
which makes clear a methodology for
delivering quality education at a distance. Distance education methods can
and do vary substantially.
The lack of empirically validated best
practices for distance education contributes to hesitation in using this education delivery medium. Additionally,
questions about identifying target audiences—how to find the individuals or
groups of individuals best suited to
ABSTRACT. Delivering MBA statistics in the online environment presents significant challenges to education and students alike because of
varying student preparedness levels,
complexity of content, difficulty in
assessing learnng outcomes, and faculty availability and technological
expertise. In this article, the author
suggests a process model that develops key learning strategies for the
online environment and offers accreditation- and literature-based guidelines for overcoming obstacles to
sucessful mastery of MBA-level statistics. Intended for faculty members,
administrators, and students with limited experience in online education,
the model may stimulate discussion
and investigation of successful practices appropriate for teaching business statistics.
become distance learners—make the
whole issue of distance education rather
intimidating. Regardless, the “train has
left the station,” and not boarding it
proactively and knowledgeably seems
an inappropriate course of action.
Growth of Distance Education
Programs and Courses
The Council for Higher Education
Accreditation (2002a) estimated collegiate enrollment in distance education
at 2.2 million students in 2002. Thirtyfive of the 50 states in the United States
already have statewide virtual universities. Of 5,655 accredited academic
institutions, 35% offered some form of
distance learning programs or courses.
Of these, 86% held regional accreditation. These data attest to the regional
accrediting bodies’ leadership in recognizing the legitimacy of distance education as a viable alternative to traditional on-campus program and curriculum
delivery (Council for Higher Education
Accreditation, 2002b).
Smith (2001) listed many of the benefits that distance education provides
for both students and faculty members.
For students, these include accessibility,
flexibility, participation, absence of
labeling, written communication experience, and experience with technology.
Faculty members enjoy the same benefits and perhaps employment advantages derived from newly gained skills.
There are difficulties as well, notably
issues concerning interaction and team
building, administration of examinations, absence of oral presentation
opportunities, and technical problems.
From the faculty perspective, the time
requirement is significant, and activities
include designing courses, learning new
technologies, and resolving technological problems. All these factors contribute to the strong support for and
rapid development of distance education in many settings, as well as the
indifference or hesitation in others.
March/April 2004
237
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Best Practices
Academic quality, driven by the successful enabling of learning, requires
interaction among faculty members and
students. Shea, Motiwalla, and Lewis
(2001) reported that top students,
although generally satisfied with the
distance education environments, desired more frequent and faster interaction (meaning feedback) with professors. The Council for Higher Education
Accreditation (2002b) suggested that
programs should “develop a sense of
community through study groups”
(p. 12). The eight regional accrediting
commissions reinforced this view,
emphasizing the “community of learning” perspective that suggests that faculty members be involved cooperatively
in course creation and delivery and that
learning be dynamic and interactive,
regardless of the setting (Middle States
Commission on Higher Education,
2002). In its study of benchmarks for
success in distance education, the Institute for Higher Education Policy (2000)
qualified interactivity as the “sine qua
non for quality” (p. 16).
Larson (2002) argued that whereas
traditional education is centered on the
professor, distance education is inherently student centered. This suggestion
has a rather profound implication:
Although the instructor may continue to
orchestrate the course, the students
actually may direct the learning process,
relying on their own initiative and
frames of reference. Recognizing this
perspective suggests questions about
how to differentiate these roles. For
example, what boundaries should the
course establish, and how much flexibility should it allow? This paradigm
requires faculty members to mentor,
facilitate, and enable while ensuring
mastery of fundamental and rigorous
statistical concepts and methods (in this
case) for which creativity and innovation may be problematic.
In the traditional setting, students discuss and exchange ideas about course
topics in and out of the classroom. Faculty members are involved in many of
these occasions, and they add lecture
interaction and office hours. Boose
(2001) suggested that professors should
first ask themselves how they can repro238
Journal of Education for Business
duce the classroom experience or design
a course structure that accomplishes
learning objectives differently. In an
online
environment,
replicating
exchange opportunities requires planning, monitoring, and participating in
all such opportunities to ensure knowledge is being exchanged and understood. Faculty members cannot simply
observe, as in a classroom.
The most difficult portion of the traditional environment to re-create is
interactive lecture. Although an instructor may rely on written notes to ensure
logical flow of presentation and discussion, much of the spoken word in a
classroom is not scripted, frequently
because it is spontaneous. Yet without
the unscripted dialogue, learning may
be incomplete; hence, special attention
must be afforded its proxy. The lecture
material links the textbook and related
readings and facilitates student participation in all available media. Whatever
the number and extent of activities
designed to replace classroom lectures,
online course professors must ensure
their consistency, comprehensiveness,
and clarity.
In its comprehensive review of the literature and a survey of faculty members, students, and administrators at
institutions recognized for their extensive experience and high-quality distance learning programs, the Institute
for Higher Education Policy (2000)
identified and then confirmed distance
learning program benchmarks in seven
categories: institutional support, course
development, teaching/learning, course
structure, student support, faculty support, and evaluation and assessment.
Course developers should use desired
learning outcomes to determine the
technology to be used, and courses
should be designed to engage students
in the practice of desired competencies.
Effective distance learning requires
facilitation of student interaction, including timely feedback for questions
and assignments and instruction in
effective research methodologies.
Course structure should ensure that students receive adequate information
about the distance learning aspects of
the program or course. This should
include supplemental information about
objectives, concepts, ideas, and out-
comes, as well as expectations concerning timely submission of assignments
and reasonable timeframes for grading
and feedback. Student support should
include hands-on training on courseware, library access, course software,
and the like. For the area of evaluation
and assessment, findings suggest application of specific standards measured
via several assessment methods.
The School and the Course
Bloomsburg University, located in
central Pennsylvania, is one of 14 universities in the state’s system of higher
education; it serves a primarily undergraduate population of approximately
7,500 students drawn from neighboring
communities and small cities. The MBA
program has an enrollment of approximately 100 students who have the option
of completing a “generic” MBA or
choosing a concentration. Statistical
Analysis and Design is a foundation
course in the program typically taken by
MBA students. Before summer 2002,
none of the MBA courses at Bloomsburg
were offered via distance education.
Environmental Context
The impetus for developing an online
version of this particular course derived
from three distinct sources. Studies
(Eaton, 2001; IHEP, 2000) suggested
that individual faculty members seeking
personal development in technological
learning enhancements or seeking to
satisfy innate desires for new knowledge initiate online courses and even
development of entire online programs.
A second reason for considering online
course development at Bloomsburg was
a predicament resulting from traditional
delivery of the MBA at a location 50
miles from the main campus. Having
made the commitment to allow potential
students to complete MBA degree
requirements within a definite timeframe and without requiring attendance
at the main campus, the participating
departments’ faculty resources were
being stretched to the limit as they
struggled to ensure that all courses were
taught by instructors with doctoral
degrees. Finally, in an effort to increase
the growth of the program and decrease
the time required to complete it, program administrators determined to offer
some or all of the foundation courses
during summer sessions. Because very
few of Bloomsburg’s MBA students are
full-time, offering students a way to
meet family and work commitments in
addition to completing course work
seemed reasonable. Making foundations
courses more accessible (via distance
education) enables new students to
enroll in core courses in their first fall
semester, thereby speeding the time to
degree completion.
• Learn and understand the purpose,
value, and methods of descriptive
statistics
• Learn and understand key concepts
of probability theory
• Learn and understand the purpose,
value, and methods of inferential
statistics
• Transform data into management
information using probability and
statistics
These objectives become the foundation
of assessing learning outcomes and
drive the development of the appropriate online capabilities.
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Statistical Analysis and Design
My pedagogical approach to the business curriculum concurs with Markel’s
(1999) view that pedagogy is not
instructional-mode dependent. My
approach, interpreted specifically for an
MBA statistics class, can be stated as
follows: The class should impart a solid
foundation of knowledge and theory
and a thorough understanding of statistical methodologies through extensive
demonstration of applications to forprofit and not-for-profit organizations.
By integrating research, writing, presentation, and computer skills, along with
case analyses and technical methodologies in the learning experience, students
successfully progress from learning why
certain knowledge is necessary and
important, through learning what the
knowledge actually includes, to learning how to use and apply it. Ideally,
graduates will have a general knowledge of business statistics and will
know more than what to do and how to
do it—they will know why they are
doing it.
This pedagogy drives the development of the course objectives and content, as well as its structure and assessment techniques.
Content
The content for this course consists of
typical topics ranging from descriptive
statistics to multiple regression. Although the literature suggests that some
of these should be replaced by more
applied topics, such as quality control or
time series, this issue is not a key source
of discontent in Bloomsburg’s program
because these additional topics are covered elsewhere in the program.
Expectations
A recurring theme in the research literature and accrediting guidelines is the
need to make clear to students what is
expected, not only in terms of output
(homework, papers, etc.) but also in
terms of the level of knowledge and
ability that they should achieve. Phipps,
Wellman, and Merisotis (1998) suggested clearly identifying a set of outcomes
in terms of expected student knowledge,
skills, and competency levels. Advising
students about the program course, its
objectives, concepts, ideas, and outcomes, is a key practice among the most
successful practitioners of distance education (IHEP, 2000).
Transitioning the Course
In Table 1, I list the principal opportunities for interaction in the traditional
learning environment and propose corresponding alternatives in the computermediated (Internet) learning environment. These opportunities cover formal
and informal professor-to-student exchanges as well as corresponding student-to-student opportunities. Assuming
acceptance of the preceding discussion
on the importance of interactivity in the
learning process, a key challenge for faculty members teaching in online courses
is the transformation of traditional
opportunities by effective use of online
capabilities. Experience suggests that
this is no easy task; a semester of seemingly innumerable e-mail messages
made this quite evident to me.
Care in transforming the standard traditional business statistics class is
essential. Otherwise, existing students
could be alienated, with a potential negative impact on planned program
growth. Despite the availability of distance learning course software within
the state system without any explicit
cost to individual member universities,
there had been very little activity at this
campus in this arena; hence, a gradual
phase-in, from the Web-enhanced
(blended) course to the Web-based
(online) course, was chosen as the
appropriate strategy. I used this
approach by adding Web enhancements
to all of the courses that I taught, both
graduate and undergraduate, on and off
TABLE 1. Interactions in Traditional and Online Learning Environments
Behavioral Objectives
Traditional
Each course in the College of Business has behavioral objectives—in other
words, what students are expected to do
with the knowledge gained in the course.
The objectives that follow represent the
knowledge, skills, and abilities that students are expected to achieve in this
course regardless of the delivery media:
Lectures
Assignments
Question and answer
Formal discussions
Informal discussions
Office visits
Phone calls
E-mail messages
Online
Learning units
File exchange
Discussion forums, chat rooms
Discussion forums, chat rooms
Chat rooms, group pages
Chat rooms
Phone calls
E-mail messages
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239
TABLE 2. Course Web Site Structure
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Course information
Syllabus
Calendar
File names
Word file format
Excel file format
Online chats
Practice quizzes
Midterm examination
Final examination
Faculty and course
evaluation
Faculty information
Introduction
campus. Students began to appreciate
the learning or improving of skills related to time management, technology, and
electronic communication.
Initially, only distribution of documents—syllabi, course outlines and
schedules, assignment guidelines and
formats, and any supplemental reading
material or information—was handled
via the course Web site. Next, I added
announcements dealing with course
requirements, learning opportunities,
assignment submissions, and examinations. After these, I added discussion
forums for questions about homework
and other class activities, and finally, I
created examinations for online applications and scheduled them in on-campus
computer labs.
Realizing that student feedback was
essential to additional support for further
distance learning activities, I developed
a multiquestion supplement to the
approved student evaluation administered each semester. My questions
addressed students’ use and opinions of
the online courseware and course-related
software. Results showed significant
interest in and appreciation of the Webbased enhancements. Having supporting data before I initiated the online
courses proved a significant benefit,
because the contractual environment at
Bloomsburg commands supplemental
payments for online course development and teaching. Budgets are usually
240
Journal of Education for Business
Assignments
Homework
assignments
Homework
exercises
Best of class
Research paper
Course documents
Introduction
Learning objectives
Tasks
Descriptive statistics
Learning objectives
Visual statistics
Tasks
Probability
Discrete random variables
Continuous random variables
Sampling distributions
Confidence intervals
Hypothesis testing
Two-sample statistical inference
Experimental design and
analysis of variance
Simple linear regression analysis
Multiple regression analysis
an issue, and fortunately, opinions were
not the deciding factors.
Online Version
The online version of Statistical
Analysis and Design at Bloomsburg
University does not have a unique
course description. An advertisement
describing the mechanics of the course,
the time frame in which it would occur,
and the faculty member overseeing its
development and delivery was made
available to all current graduate students
and all new program applicants.
Upon registration, students received
information about how the course would
be conducted; they were also advised of
required attendance at two on-campus
sessions. During the first session, held in
a computer lab at the beginning of the
course, students were walked through the
course software (in this case, Blackboard). They enrolled themselves in the
course Web site and practiced with all of
the courseware features that would be
used: announcements, discussion forums,
file exchanges, virtual classrooms, and
other student tools. These initial sessions
ensured that all students had at least a
basic level of familiarity with and competency in courseware functions. The second session consisted of an in-lab software demonstration that was scheduled
to coincide with the teaching of relevant
course content.
Books
Business Statistics
in Practice
Other readings of
interest
The course Web site was organized
through Blackboard’s existing framework. In Table 2, I depict this structure.
Note the detail provided in the course
information section, the variety of
interaction opportunities listed under
“Communication,” and the information
provided concerning assignments, all
of which were intended to facilitate the
community of learning mentioned
previously.
To maintain timely, sequential, and
cumulative learning of the course’s content, I made material available on the
various Web pages 1 week before it was
listed in the course schedule. This prevented students from getting too far
ahead of their peers or simply attempting to complete all of the graded work
in a very short time period, forfeiting
the benefit of student and faculty interactions. For this same reason, all assignments had due dates that could not be
exceeded without penalty. Students did
have the option to submit their work
earlier if personal or work conflicts precluded them from submitting on the
actual due dates. Initial submissions for
discussion forums had mandatory posting dates, which helped alleviate the
common difficulty of later postings’
sounding too much like earlier ones.
Additionally, all students were required
to attend at least 1 hour of online chat
(virtual classroom) per week, during
which they were to present problem
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Web resources
Communication
Research Web resources
Send e-mail
Problem solving and
Discussion board
statistics Web resources
Virtual classroom
Roster
Group pages
solutions, answer the instructor’s questions, contribute to discussions, and ask
any unresolved questions.
The issues of originality, interactivity, and applicability are important in all
courses. In the online environment,
these become even more important, as
students do not have the opportunity
for simultaneous discussion and observation. For example, in a traditional
classroom, the instructor can draw a
graph and respond to comments or
questions as the picture unfolds.
Although this is possible during a virtual classroom, time constraints usually limit these opportunities. For this
reason, along with a pedagogy that
encourages providing both analytical
and visual explanations of key concepts, techniques, and methodologies,
choosing a textbook that included an
Excel add-in (with tutorials in chapter
appendixes) and supplementary software that provided graphic depictions
of all covered statistical concepts
proved critical. Another useful technique was the weekly requirement that
students either discuss, in jargon-free
language, a particular concept or analytical result or actually conduct an
experiment using a statistical technique
(e.g., ANOVA or regression) and prepare a report for the discussion forum
for all other students to see and discuss. The following is an example of
this requirement:
Student tools
Digital drop box
Edit your homepage
Personal information
Calendar
Check grade
Manual
Tasks
Electronic Blackboard
Address book
After thoroughly reviewing Section 7.3,
Sample Size Determination, create a business scenario that requires determination
of a sample size for estimating a particular population mean. Refer to the formula
on page 257 of the text, and read the paragraph directly under it. Choose a reasonable value for B (error bound), and follow
the second alternative for estimating the
population standard deviation described
on page 259. Then compute sample sizes
for both a 95% and a 99% level of confidence. To respond successfully, you must
provide a clear and concise narrative
describing this particular business scenario, including the characteristic of
interest, the unit of measurement, justification for the error bound value chosen,
calculation of the standard deviation estimate, the sample size results, and onesentence “translations” for each.
Note that this example is perfectly suitable and appropriate, yet optional, for a
traditional MBA statistics class. In the
online environment, the option becomes
a necessity.
In addition to the weekly discussion
forums, students participated in a variety
of assignments that gave them opportunities to learn via their own information
filtering system—visual, auditory, or
kinesthetic (Torres, 1985). They solved
homework problems, responded to
inquiries from the instructor in online
chat rooms, prepared case analyses and
received both individual and summary
feedback, researched and prepared a
report on the misuse of statistics in the
media, completed instructor-prepared
tutorials in course-related software,
reviewed outstanding work from other
students, initiated group discussions or
online chats, and read textbooks, supplemental articles, instructor notes,
PowerPoint presentations, and other
Web sites. Each content lesson contained a list of tasks detailing pertinent
student activities.
Assessing Learning Outcomes
Use of multiple assessment techniques is necessary to derive reliable
results specific to individual students.
My pedagogy incorporates this perspective regardless of environment. The
intent is to enable students to succeed
and to provide ample opportunity for
them to demonstrate their understanding of and ability to apply the course
content. Measuring how well students
achieve the behavioral objectives of an
MBA statistics course requires frequent
and varied assessments of direct applications of statistical methodology and
techniques to practical business problems, issues, and opportunities.
Compared with the traditional version of the course, the online version
has nearly identical assessments; however, their frequency and intensity are
greater. For example, although both
delivery formats require that an Excel
spreadsheet solution to a homework
exercise contain adequate explanations
for all methods and results, the two formats handle discussions in very different ways. In the traditional classroom,
instructors observe and listen to students’ comments and perceptions. In the
online environment, comments must be
written, which usually requires more
effort from both the students and
instructor. An advantage of the virtual
classroom feature is the automatic
archiving of all communications. Faculty members and students can access
transcripts of past chats to determine
levels of participation and accuracy or
to review guidance and explanations.
This is similar to automatic note taking
and attendance recording.
A comparison of examination results
from online and traditional sections of
this course (run in nonconsecutive
semesters) shows conflicting results
from this assessment measure. Applying
March/April 2004
241
242
Journal of Education for Business
Traditional
Online
50
25
Sig
nif
ica
nc
e
Ind
ep
en
de
nc
e
En
co
ura
ge
me
nt
Cla
rity
Ev
alu
ati
on
Gr
ad
ing
0
Pre
pa
red
Percent Responding in Highest Categories
75
En
thu
sia
sm
FIGURE 1. Student evaluations: Comparison of traditional versus online
instructor-based traits.
100
Traditional
Online
75
50
25
Co
mm
un
ica
tio
n
Un
de
rst
an
din
g
Ab
ility
0
Kn
ow
led
ge
Several studies (Council for Higher
Education, 2002a; IHEP, 2000; Phipps
& Merisotis, 1999) suggested the
importance of measuring overall student
satisfaction with the distance learning
experience in addition to assessing
learning outcomes. Students in both the
traditional and online course offerings
completed identical versions of
Bloomsburg’s standard faculty and
course evaluation. All students also
completed a supplemental survey
addressing the Web-based and software
features available within the course
structures.
In Figure 1, I show comparative
results for these identical surveys from
the same two course sections used for
the comparisons of test scores. These
results for the questions pertaining to
instructor-based traits are virtually identical. Small enrollments precluded z
tests of these respective proportions.
In Figure 2, I show results typically
associated with the learning that occurs,
including questions about students’ selfperceptions of knowledge, ability,
understanding, comprehension, and
communication. With the exception of
communication skills, results were
again similar, with differences due to
responses from one or two students (of
the 11 or 12 in these sections). Note also
that the results do not include students
“in the middle,” but only those responding in the two most positive response
categories (on a five-point scale).
Finally, the data in Figure 3 suggest
several interesting and beneficial advantages based on students’ perceptions
about various course tools, corollary
competencies, and personal learning
100
Co
mp
reh
en
sio
n
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Student Satisfaction
Percent Responding in Highest Categories
the small sample t test for the means
suggests no significant difference
(H0: µ1 – µ2 = 0) between these two
groups of students in scores from identical midterm examinations (p value =
.26), whereas the same hypothesis can
be rejected (p value = .02) for the final
examination. Like so many other empirical reports (Russell, 2002), these results
are inconclusive because of their conflicting implications. Also, relying solely on test scores is a recognized weakness of learning outcomes assessment.
FIGURE 2. Student evaluations: Comparison of traditional versus online
learning perceptions.
strategies. The response scale for these
items did not include a middle ground
(the scale included strongly agree,
somewhat agree, somewhat disagree,
and strongly disagree). In fact, the only
item on which online students expressed
lesser preferences than traditional stu-
dents pertained to Blackboard-delivered
course examinations. This lower satisfaction correlates with the inconclusive
(weak) results of statistical tests discussed previously. Otherwise, they rated
the online experience higher than its traditional counterparts on improvement of
Traditional
Online
50
25
0
of
Inf
orm
ati
on
Te
Ex
ch
am
no
log
ina
yC
tio
ns
om
mu
nic
ati
on
Sk
ills
Co
mp
reh
en
sio
So
n
ftw
are
Pre
fer
en
ce
So
ftw
are
Te
Im
ch
pa
no
ct
log
yD
ec
isio
nS
kill
s
Percent Responding in Highest Categories
75
Dis
se
mi
na
tio
n
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100
FIGURE 3. Student evaluations: Comparison of traditional versus online
course tools and corollary skills.
al (regional) and specific (AACSB) levels, which include the need for interactivity among students and faculty members, is an appropriate strategy. My
original intent—to create at least as
many opportunities for the exchange of
information (see Table 1) in the online
environment as there are in the traditional classroom—appears viable in terms of
enabling student learning. Additionally,
the improvement in corollary skills (e.g.,
communication and technology) may be
substantial and suggests a valuable
advantage for successful online students.
Quantitative methods for assessment
of differences in learning outcomes were
limited in this application. Test scores
are only one of the many assessment
techniques typically employed, and student evaluations vary from institution to
institution. Qualitative techniques for
assessing differences may provide additional insights to true learning outcomes
and supplement the numerous “no significant difference” findings already
published in the literature.
REFERENCES
their technology-based communication
and decision-making skills and on the
value of the accompanying application
software. Results were nearly identical
for items pertaining to the value of the
Web site for disseminating information
and its contribution to learning course
content. Note that the traditional course
from which these data derive did incorporate the Blackboard platform and use
of basic Web capabilities in addition to
the weekly class meetings.
Conclusion
The benchmarks established in the
IHEP (2000) study were applied in the
successful transition of this course to
the distance format. Desired outcomes,
stated as behavioral objectives in the
course syllabus, directed the course
structure to levels of technological competence that all students could be
expected to have. The activities for students, the software tutorials, required
research, self-designed experiments,
discussion forums, homework exercises, and examinations, motivated (in fact,
required) students to become engaged
fully in the application of statistical
methods to business contexts. Students
knew expectations in terms of the volume and level of work, as well as the
time constraints and submission
requirements. The instructor remained
cognizant of and responsive to timely
(i.e., rapid) and constructive feedback.
Students received early and repeated
information about the course, how it
worked, and the different activities that
they would experience. Results were
assessed through a variety of techniques, which allowed every student to
demonstrate his or her grasp of the
material in the way that enabled each
student best.
This case review suggests that pedagogy is not necessarily a function of
delivery environment. This transition of
an MBA statistics course to an online
format was driven by professional development, cost, and student convenience factors, without a strategic initiative to create an online program of study,
and the results of this study show that
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