Chapter 1
Statement of the Problem
Course management systems are becoming
ubiquitous in higher education. A 2004 survey of a random sample of academic
department chairpersons showed that 89.1% of their departments had adopted the
use of a course management system (Harrington, Gordon, & Schibik, 2004).
Although they are commonly used in
support of distance education, course management systems also enjoy widespread
use as support for face-to-face classes. In the same survey, 44.1% of the
respondents indicated that the course management system was used primarily in
support of face-to-face courses (Harrington et al., 2004).
Course management systems can be used in
a number of ways to support face-to-face courses. The first and most common
use is for course organization. Course management systems provide a myriad of
organizational tools. Among them are tools for managing course content and
assignments, course schedule, materials, and resources. Course management
systems also provide communication support, with features such as announcements
and ÒwhatÕs newÓ, course email, discussion forums, chat rooms, and gradebook.
Another course management feature is support for online quizzes, tests, and
surveys.
The many features of course management
systems have helped to facilitate a new type of course structure called a
hybrid course. A hybrid course is a face-to-face course in which some of the
class meeting time and activities are replaced by on-line activities, such as
discussions. The result is that some of the learning activities in the course
are asynchronous; that is, not in the real-time classroom setting and time
frame. Hybrid courses have become more and more popular as people recognize
the value of asynchronous learning in general, and not just for online courses
(Voos, 2003).
Studies have shown that students and
faculty both like working with course management systems (ANGEL, 2002; Basile
& DÕAquila, 2002; Carnevale, 2003; Morgan, 2003). But students and faculty
cannot benefit from the use of course management systems unless the faculty
adopt them. In many institutions, adoption of a course management system is
voluntary (Morgan).
During the summer, before the beginning
of the 2004/2005 school year, Rose-Hulman Institute of Technology adopted the
ANGEL course management system, and offered it to the faculty for their use. A
series of workshops was held at several different times, in order to provide
training for the potential users. These workshops included: Workshop 1: The
Basics, Workshop 2: Quiz and Communication, and Workshop 3: Gradebook and Assessment.
Adoption of ANGEL was voluntary.
Rose-Hulman is a private, coeducational,
non-sectarian college of engineering, science, and mathematics, located in
Terre Haute, Indiana. During the 2004/2005 school year, Rose-Hulman had an
enrollment of 1,904, including 1,765 undergraduate and 139 graduate students.
Of the undergraduate students, 99% were full-time students and 59% lived on
campus. The average student age was 19.7 years. Rose-Hulman employs 150
faculty members. Of those faculty members, 98% have a doctoral level degree,
96% are full-time employees, and 60% are tenured. Bachelor of Science degrees
are offered in fourteen areas in engineering, science, and mathematics, as well
as in economics. Master of Science degrees are offered in eight areas.
Rose-Hulman courses are face-to-face; there are no distance education programs
offered. Rose-Hulman enjoys a reputation for academic excellence and places a
major emphasis on teaching.
Studies have shown that adopters of
innovations, including new technology, fall into five categories (Rogers,
2003): innovator, early adopter, early majority, late majority, and laggard.
Each adopter category consists of individuals with a similar degree of
innovativeness. Rogers defines innovativeness as Òthe degree to which an
individual or other unit of adoption is relatively earlier in adopting new
ideas than the other members of a systemÓ (p. 22). The adopter categories
themselves are thus a means of convenience in describing the members of a
system.
The adopter categories form a continuum,
from those with the most innovativeness to those with the least. Like many
other human traits, such as height weight, or intelligence, innovativeness has
been found to be normally distributed. Thus, the innovativeness of a
population can be represented by a bell-shaped curve. An example of an
innovativeness curve is shown in Figure 1.1. The areas represented by each of
the adopter categories are noted on the figure. Innovators and early adopters
make up the first 16%, the early majority rounds out the first half, the late
majority the next 34%, and laggards make up the final 16%. The numbers on the
bottom of the graph indicate the number of standard deviations from the norm.
A
brief description of the characteristics of these categories as they relate to
educational technology is (Hall & Elliott, 2003):
- Innovators: They are the campus
ÒtechiesÓ who are intrigued by new hardware and software and are able to
learn the new technology on their own.
- Early Adopters: They are viewed as
visionaries who combine their competence and desire in integrating new
technologies into the classroom and teaching process.
- Early Majority: They are receptive
toward new educational technology, but only after it has been shown to
enhance teaching and student learning.
- Late Majority: They are skeptical
toward the use of new educational technology and must be convinced or even
coerced to use it in the classroom.
- Laggards: They have absolutely no
interest in using new technology in the classroom and may express
opposition to its implementation.
When a new technology is made available,
the innovators would be expected to adopt it quickly, followed by the other
groups in order. Most people have a view of themselves as a certain type of
adopter of new technology. They see themselves as responding in a consistent
manner to the introduction of any type of technology. They have a feel for
their degree of innovativeness, and tend to see themselves as falling somewhere
along the adoption category spectrum. One need only query the World Wide Web
with a search engine to encounter phrases such as: ÒIÕm an early adopterÓ or
ÒIÕm usually in the late majorityÓ.
People appear to view themselves as
responding fairly consistently to technological innovation, in line with their
degree of innovativeness, which represents their adopter category. Perhaps
this information could be used to predict future adoption of course management
systems and other educational technology. For instance, if someone considers
himself or herself to be an innovator or early adopter when it comes to
technology in general, is he or she more likely to adopt a course management
system? Conversely, would someone who considers himself or herself to be a
late adopter or laggard in adopting technology in general be less likely to
adopt a course management system?
An
instructorÕs teaching style can also affect his or her adoption and use of
technology (Grasha & Yangarber-Hicks, 2000). Zisow (2000), reflecting on
20 years of teaching experience, contends that, Òthe greatest factor affecting
whether a teacher does or does not use technology in the classroom, is teaching
styleÓ (p. 36). Teaching style may influence an instructor to resist new
technology, or to adopt it with enthusiasm.
GrashaÕs research led him to identify
five teaching styles that were commonly found in higher education: expert,
formal authority, personal model, facilitator, and delegator. These styles are
described below (Grasha, 1994):
- Expert: Possesses knowledge and
expertise that students need. Strives to maintain status as an expert
among students by displaying detailed knowledge and by challenging
students to enhance their competence. Concerned with transmitting
information and ensuring that students are well prepared.
- Formal Authority: Possesses status
among students because of knowledge and role as a faculty member.
Concerned with providing positive and negative feedback, establishing
learning goals, expectations, and rules of conduct for students.
Concerned with the Òcorrect, acceptable, and standard ways to do thingsÓ
(p. 154).
- Personal model: Believes in
Òteaching by personal exampleÓ and establishes a prototype for how to
think and behave. Oversees, guides, and directs skill acquisition by
showing how to do things, and encouraging students to observe and then to
emulate the instructorÕs approach.
- Facilitator: Emphasizes the
personal nature of teacher-student interactions. Guides students by asking
questions, exploring options, suggesting alternatives, and encouraging
them to develop criteria to make informed choices. Overall goal is to
develop in students the capacity for independent action and
responsibility. Works with students on projects in a consultative fashion
and provides much support and encouragement.
- Delegator: Concerned with
developing studentsÕ capacity to function autonomously. Students work
independently on projects or as part of autonomous teams. The teacher is
available at the request of students as a resource person.
According
to Grasha (1996), an instructor will possess each of the five styles, although
to varying degrees. Do adopters of educational technology tend to possess some
of these styles to a greater degree than nonadopters?
Purpose of the Study
This study examined the
adoption and non-adoption of the course management system ANGEL by the faculty
at Rose-Hulman, in relation to the following faculty characteristics: degree
of innovativeness, as represented by adopter category index (along the
continuum of: innovator, early adopter, early majority, late majority,
laggard); and teaching style (ratings for: expert, formal authority, personal
model, facilitator, delegator). It also included descriptive data on the reasons
for adoption and nonadoption of the course management system, and how the
adopters use the features of that system. This study explored the following
questions:
- What are the differences in adopter
characteristics and teaching styles of adopters and nonadopters of the
ANGEL course management system?
- What are the reasons for adoption
and nonadoption of the ANGEL course management system?
- How do adopters use the ANGEL course
management system?
Need for the Study
Although
there have been a number of studies involving the use of course management
systems, few have been found that study the issues of degree of innovativeness,
as represented by adopter category, and teaching style with relation to the
adoption of a course management system.
Significance of the Research
The answers to the research questions
should provide information that is beneficial to college administrators. A
potential benefit of the study is that schools might better anticipate the
level of adoption of course management systems, and perhaps other educational
technology, by their faculty members. Knowing who the likely adopters and
nonadopters are could have a number of advantages.
By knowing who the likely nonadopters
are, schools might focus on them in advance, helping them to become comfortable
with the technology and helping them to fit it into their teaching styles
(Young, 1999). Schools could anticipate how many nonadopters there are likely
to be and could target that group well in advance, with orientation meetings or
other soft-sell methods, so that when the technology is introduced, the
transition might be smoother, and more faculty might adopt it early. Schools
might even use the results of this study to guide hiring practices, perhaps
giving hiring preference to faculty who are more likely to adopt new
educational technologies.
By knowing who are the likely adopters,
schools might tailor early workshops and training materials for them. These
early workshops might have more of a Òquick startÓ focus, for instance. More comprehensive
workshops might be held for the later adopters. That way the early adopters
should be less likely to be bored, and the later adopters should be less likely
to be feel that the training is moving too fast and leaving them behind.
Definition of Terms
ANGEL
adopter: A faculty member who chose to adopt ANGEL when it was first offered,
and who uses or has used ANGEL in support of a course at Rose-Hulman in an
ongoing capacity, as opposed to using it for a one-time event such as a survey.
ANGEL nonadopter:
A faculty member who chose not to adopt ANGEL when it was offered.
Course
management system: A software system that allows a student to interact in both
synchronous and asynchronous fashions with course materials and with other
students and with the instructor.
Assumptions
It
is assumed that the adopter category rubric accurately assessed the faculty
memberÕs perception of his or her degree of innovativeness and adopter
category, and that the teaching styles inventory accurately measured the faculty
memberÕs teaching style. It is assumed that the faculty were truthful in
their responses.
Limitations
The
study was limited to one institution. Confining the study to one institution
may affect the generalizability of the results.
Delimitations
This
study was limited to Rose-Hulman Institute of Technology. However, there were
circumstances that made the limitation of the study to Rose-Hulman an
attractive one. The ANGEL course management system was introduced for the
first time in late summer 2004, so the timing was ideal for a study of the
initial adoption of a course management system.
The
adoption of ANGEL was completely voluntary. There was no internal or external
pressure on faculty members to adopt. Because this was the first year of ANGEL
use at Rose-Hulman, new faculty members did not enter an environment where
there were already a lot of ANGEL users. Thus, there was no peer pressure from
existing users.
Also,
Rose-Hulman does not have a distance education program, so there was no reason
for faculty to adopt ANGEL with the view that the expertise would be needed
later in teaching distance education courses.
Due to
all of these circumstances, it was felt that the Rose-Hulman environment would
provide a valuable opportunity to study the adoption of educational technology.
Chapter 2
REVIEW OF THE LITERATURE
This
review of the literature is divided into three parts. The first part describes
course management systems and their uses. The second part discusses diffusion
theory and educational technology. The third part focuses on teaching styles
and how they might affect oneÕs approach to the use of educational technology.
Course Management Systems
A
course management system is a software system that allows a student to interact
in both synchronous and asynchronous fashions with the course materials and
with other students and with the instructor. Course management systems have
taken higher education by storm. There may be a number of reasons for this,
among them that they are relatively easy to learn and easy to use. They help
smooth the transition from teaching without technology to teaching with
technology (Kuriloff, 2001). Studies have shown that students are comfortable
in courses that use course management systems, as compared to traditional
courses (Basile & D'Aquila, 2002). Although they are commonly used in
support of distance education, course management systems can be used in a
number of ways to support face-to-face courses, as described below.
Organizational Tools
The
first and most common use of course management systems is for course
organization. Faculty express appreciation for the ability to easily organize
course materials (Carnevale, 2003; Morgan, 2003), and students find them
helpful in managing their workloads (ANGEL, 2002). Course management systems provide a myriad of
organizational tools. Among them are tools for managing course content and
assignments, course schedule, materials, and resources.
Course
Management Systems are sometimes used in large-enrollment classes to ease the
organizational burden of managing such a class and to increase communication
between instructor and students (Cervato, 2003; Morgan, 2003). A study at Duke
University (Blackboard,
2003) found the most commonly used features were content posting features,
collection of links, and the online gradebook. A similar study at Penn State (ANGEL, 2002) found the features considered
most useful to be the lesson tab, on-line syllabus, and calendar.
Communication Tools
Course
management systems also provide communication support, with features such as
announcements and ÒwhatÕs newÓ, course email, discussion forums, chat rooms,
and gradebook. In addition, course management systems provide support for
online quizzes, tests, and surveys. Faculty use course management systems to
communicate easily with students and to increase the amount and timeliness of
feedback (Morgan, 2003). Students find that a course management system enables
them to communicate more easily with faculty (ANGEL, 2002).
Hybrid Courses
The many features of course management
systems have helped to facilitate a new type of course structure called a
hybrid course or Ð in some of the literature Ð a blended course. A hybrid
course is a face-to-face course in which some of the class meeting time and
learning activities are replaced by online activities. The goal of the hybrid
course is to join the best features of in-class teaching with the best features
of online learning to promote active independent learning and reduce class seat
time (Garnham & Kaleta, 2002). Hybrid courses have become more and more
popular as people recognize the value of asynchronous learning in general, and not
just for its use in online courses (Voos, 2003).
A course management system provides
support for the online portion of a hybrid course. To create a hybrid course,
the instructor must first determine which parts of the traditional course
should be placed online. Some typical online components of hybrid courses are
case studies, tutorials, tests and quizzes, simulations, discussions, and group
collaborations.
A hybrid course format can be used for
both small and large class sizes, in any discipline. In the recent Hybrid
Course Project at the University of Wisconsin Milwaukee (Garnham & Kaleta,
2002), the participating instructors represented a wide variety of disciplines,
and class size ranged from under fifteen to more than two hundred. The hybrid
activities replaced 25 to 50% of class time. This reduction was made in many
ways. In some classes, a portion of the class time Ð for example, one class
period Ð was eliminated every week. Other classes met for several weeks and
then did not meet for several weeks. Some instructors shortened long classes
and used the remaining hours for online activities.
Hybrid courses offer many advantages over
face-to-face courses. Perhaps the most important and far-reaching is student
engagement with the course materials. A hybrid course requires a student to be
an active participant in his or her learning, rather than a passive recipient
of knowledge. This active learning is a component of meaningful learning,
which is discussed in the next section. Another advantage, as discussed in the
previous section, is increased communication among students and between student
and instructor. There is a continuity of communication in both online and
face-to-face settings. This helps to build a sense of community in the class,
another factor in the facilitation of meaningful learning.
Instructors in the Hybrid Course Project
(Garnham & Kaleta, 2002) felt that hybrid courses enabled them to
accomplish course learning objectives more successfully than traditional
courses did. They also believed that their students learned more in the hybrid
classes, that they produced better papers and projects, performed better on
exams, and had more meaningful in-depth discussions of the course material.
Time flexibility is a popular feature of
hybrid courses. Both students and instructors appreciate the ability to
schedule coursework flexibly. They also appreciate the decrease in time and
expense spent on commuting and parking (Garnham & Kaleta, 2002).
Simply transferring traditional course
content onto a course management system does not create an effective hybrid
course. Hybrid courses take more time to develop than face-to-face courses
(Garnham & Kaleta, 2002). Some causal factors are the time needed to
acquire new technology skills, time needed to redesign the course in a hybrid
format, and time needed to learn new teaching techniques. The University of
Wisconsin MilwaukeeÕs Learning and Teaching Center (Hybrid, 2005) recommends allowing six months
lead time for the development of a hybrid course. But instructors who teach
hybrid courses overwhelmingly agree that they are worth the increased
development effort.
Students sometimes mistakenly believe
that the fewer class meetings of a hybrid course mean there will be less work
in the course (Garnham & Kaleta, 2002). This misconception requires an
adjustment on their part. Students can benefit from an orientation to hybrid
courses, which will better prepare them for the differences they will encounter
in these courses as opposed to traditional face-to-face courses.
Meaningful Learning
Although faculty are often attracted to
course management systems for their organizational benefits, once they are
proficient with their use, many wish to use the systems more creatively to
enhance student learning (Blackboard, 2003; Morgan, 2003). Morgan refers to this as Òaccidental
pedagogyÓ. That is, a faculty member may not have initially planned to use the
course management system for facilitating or enhancing learning; rather,
interest in the learning benefits came about later, as an unexpected outgrowth
of the organizational use.
When course management systems are used
in a hybrid course design, they have the capacity to promote what some call
Òdeeper learningÓ (Carmean & Haefner, 2002), or Òmeaningful learningÓ
(Novak & Gowin, 1984). These terms refer to learning that results in a
meaningful understanding of material and content, rather than a simple
memorization of facts and concepts.
In an effective learning environment
created with a course management system, the student can learn much more, and
can learn it more easily and enjoyably. According to Carmean and Haefner
(2002), course management systems address students' desire to learn actively
and socially at any time. They describe five characteristics of deeper
learning, which are described in more detail below: deeper learning is social,
active, contextual, engaging, and student-owned.
Deeper
learning is social (Carmean & Haefner, 2002). A course management system
supports social learning through the use of features such as discussion forums,
chat rooms, email, and bulletin boards. Students have access to course
announcements immediately upon logging in. Thus they are aware of current
issues and events in the course. Students can communicate easily with the
instructor and can receive timely feedback.
But perhaps the greatest impact on the
social aspects of learning is the ability of students to communicate with
classmates 24 hours a day. This promotes cooperation and sharing among the students,
and helps to keep students actively engaged in the course. Imagine the impact
of receiving an immediate or nearly-immediate response to a question at two in
the morning!
All of the communication features help to
foster a sense of community among students and between students and instructor.
This sense of community carries over into the face-to-face classes, and the
two serve to reinforce one another.
Deeper
learning is active (Carmean & Haefner, 2002). A course management system
supports active learning with features such as interactive assessment tools,
which allow for quick feedback. Interactive assessment tools emphasize
practice and reinforcement. The student is actively engaged with these tools,
and is in control of his or her learning. Another feature of course management
systems that contributes to active learning is their capability of offering
learning materials in many formats, such as text, audio, and video, all
available for the student to select according to his or her interest.
Deeper learning is contextual (Carmean
& Haefner, 2002). The course management system supports the principle that
learning is enhanced by integrating new knowledge into a learner's preexisting
knowledge structure. This is a feature of meaningful learning, as defined by
Ausubel in his Assimilation Theory of Learning (Novak & Gowin, 1984). The
course management system supports this with its capability of presenting many
types of information together in an integrated manner using the framework it
provides. For example, a case study activity can be set up which incorporates
sound and video clips of interviews with people addressed by the study. Or, a
real-world problem can be broken apart for students in a series of multimedia
modules.
Deeper
learning is engaging (Carmean & Haefner, 2002). The course management
system can accommodate diverse learning styles with its many presentation and
access options, its ability to handle a tremendous volume of course material,
and its 24/7 availability (Carmean & Haefner; Katz, 2003). These allow
instructors to present instructional materials in more than one way, and so
they can present them in ways appropriate for or preferred by students with
different learning styles. Multimedia options available in course management
systems enable the instructor to accommodate both visual and verbal learners,
for instance, with the use of different representations of the same content.
Student engagement is also increased when
the instructor uses the course management system to encourage individual
exploration of a course material. An enormous amount of material can be
accommodated within the course management system, and links to outside sources
can be included as well.
Deeper
learning is student-owned (Carmean & Haefner, 2002). The student has
control and ownership of his or her learning. The student must choose to
learn, and the anyplace-anytime aspect of a course management system provides
flexibility to today's busy students, encouraging them to come to the learning
environment at a time when they are most receptive to learning. The rich
integrated environment that is possible with a course management system has the
capacity to encourage and support independent discovery. This environment
affords the convenience of a single location for accessing course materials.
It also enables those materials to be presented in a variety of ways, with its
multimedia capabilities.
Potential Disadvantages
There
is a socialization curve associated with the adoption of a course management
system (Katz, 2003). As in the adoption of other types of software, there is
an initial drop in productivity as faculty and students assimilate the new
tools and methods. Some faculty members find course management systems
time-consuming and inflexible, while some students find them difficult to use
(Carnevale, 2003).
Adding
to the concerns addressed by Katz (2003) and Carnevale (2003), Kuriloff (2001)
contends that course management systems cater to the lowest common denominator.
They try to be all things to all people. They encourage instructors to merely
recreate what they do without technology, rather than using the course
management system to improve and enhance their courses. In this way, they
constrain innovation.
In
contrast to the technical issues raised above, Katz (2003) addresses a more
political issue regarding faculty perception of course management systems.
According to Katz, Òthe structure of the CMS is simultaneously an area of great
strength and one of possible resistance or even rejectionÓ (p. 54). The
reason, according to Katz is that although the course management system does
not dictate a teaching style, it does impose a structure that may be perceived
as threatening to faculty autonomy.
The
success of course management systems does not necessarily translate into
universal benefit in any setting. Ehrman and Gilbert (2003) caution that each
institution must make its own assessment of the benefits it derives from the
use of such a system. They present five questions that institutions should
consider in this assessment (Ehrman & Gilbert):
á How have the ways faculty used our course
management system improved the effectiveness of teaching and learning at our
institution?
á What specific changes in support strategy
could improve the value of a course management system?
á How are our uses of the course management
system helping to increase enrollment or retention?
á How can we use our course management
system to help control costs?
á How can faculty identify and lower
barriers to desired course management use by students?
Ehrman
and Gilbert state that the findings from studies based on the above questions
can guide efforts to increase the benefits of a course management system at an
institution.
Diffusion Theory and Educational
Technology
There
are a number of theories of innovation diffusion that have been used to study
the adoption of educational technology. These include theories that are macro
or micro in focus. Macro-level theories focus on the reform and restructuring
of educational institutions, and are called systemic change theories (Surry,
1997).
Micro-level theory focuses on the
adoption of specific educational technologies. These theories, known as
product utilization theories, focus on increasing the adoption of a specific educational
technology by a specific set of potential adopters (Surry, 1997).
Both systemic change theories and product
utilization theories can be divided into two subcategories, representing the
two predominant philosophies of diffusion of innovation in educational
technology. These are the determinist philosophy, which focuses on the
developer of an educational technology, and the instrumentalist philosophy,
which focuses on the adopter of an educational technology.
The instrumentalist theorists reject the
assumption that well-designed technologies will automatically be adopted.
Instead, they focus on the human characteristics of individual adopters. They
believe that in order to maximize the diffusion of educational technology
innovations, educational technologists should adopt a more instrumentalist
philosophy (Surry, 1997).
Diffusion of Innovation
The name most commonly associated with
diffusion of innovation is Everett Rogers. His book Diffusion of Innovations
is now in its fifth edition (Rogers, 2003). Rogers defines an innovation as
Òan idea, practice, or object that is perceived as new by an individual or
other unit of adoptionÓ (p. 12), and diffusion as Òthe process by which an
innovation is communicated through certain channels over time among the members
of a social systemÓ (p. 35).
In the case of the diffusion of
educational technology in higher education, the social system is the college or
university that is adopting the technology. The diffusion process begins when
members of the institution make the decision to adopt the innovation.
Rogers defines three types of
innovation-decisions that might apply:
1. Optional innovation-decisions: These are
choices to adopt or reject an innovation that are made by an individual
independent of the decisions of other members of the system. If the adoption
of a new educational technology is voluntary and up to the individual
instructor, this represents an optional innovation-decision.
2. Collective innovation-decisions: These
are choices to adopt or reject an innovation that are made by consensus among
the members of the system. In the case of the adoption of educational
technology, this might represent a vote by faculty on whether or not to adopt.
3. Authority innovation-decisions: These
are choices to adopt or reject an innovation that are made by relatively few
individuals in a system who possess power, status, or technical expertise. In
the case of educational technology adoption, a governing board of an
institution may make the decision to adopt a technology and then mandate its
use by faculty. This would constitute an authority innovation-decision (2003,
p. 38).
Innovation-decisions
may be influenced by a change agent. According to Rogers (2003), a change
agent is an individual who influences individualsÕ innovation-decisions in a
direction deemed desirable by a change agency. In the case of a new
educational technology, the change agent would be the individual or group that
works to influence others to adopt the technology. The change agent may target
the delivery of the technology to an intended audience, or to a subgroup of
that audience.
The
degree of the change agentÕs success in spurring the adoption of an educational
technology is positively related to the amount of effort that the change agent
expends in contacting the potential adopters and establishing a close rapport
with them. Successful change agents exhibit empathy toward their target group
and base their diffusion activity decisions primarily on the adoptersÕ needs.
They are more feedback-oriented toward the potential adopters, and work to
increase the target groupÕs ability to evaluate the new technology and make an
informed innovation-decision.
All three types of innovation-decision Ð
optional, collective, and authority Ð have the same underlying process model,
which consists of five stages. These are components of the Innovation Decision
Process Theory, and are described below:
1. Knowledge: occurs when an individual
learns of the innovationÕs existence and how it functions.
2. Persuasion: occurs when an individual
actively seeks information about the innovation and forms a favorable or an
unfavorable attitude toward the innovation.
3. Decision: occurs when an individual
engages in activities that lead to a choice to adopt or reject the innovation.
4. Implementation: occurs when an
individual adopts the innovation.
5. Confirmation: occurs when an individual
seeks reinforcement of the innovation-decision (Rogers, 2003, p. 169).
Once
the individual has adopted the innovation, he or she may decide to reject the
innovation, either to replace it with something perceived as better or to
discontinue using it due to dissatisfaction. Studies have shown that later
adopters are more likely to discontinue using an innovation than are earlier
adopters.
The
most well known aspect of RogersÕs work is probably his classification of
adopters of innovation into five adopter categories: innovators, early
adopters, early majority, late majority, and laggards (2003). The first three
categories comprise half of the adopters, and the fourth and fifth encompass
the other half. Each adopter category consists of individuals with a similar
degree of innovativeness. Thus, the adopter categories are a means of
convenience in describing the members of a system.
The adopter categories form a continuum,
from those with the most innovativeness to those with the least. Like many
other human traits, such as height weight, or intelligence, innovativeness has
been found to be normally distributed. Thus, the innovativeness of a
population of adopters can be represented by a bell-shaped curve. An example
of an innovativeness curve is shown in Figure 2.1.
Holden (2003) provides a good overview of
the characteristics of members of the adoption categories: Innovators are the
smallest category, more than two standard deviations from the mean in the
adopter categorization and represent only 2.5% of the adopters. These are
individuals who are eager to try new things and willing to take risks to do so.
They may be helpful in bringing new ideas into an organization, although
others may not be eager to follow their lead.
The next category, early adopters,
contains 13.5% and falls between one and two standard deviations from the mean.
Early adopters will quickly adopt an innovation if they feel that there is a
benefit to doing so. Others look to them as role models and are more likely to
follow their lead rather than that of the innovator in adopting an innovation.
That is, early adopters have more Òopinion leadershipÓ than do innovators
(Rogers, 2003, p. 27).
Early and late majority represent more
than two thirds of all adopters, 34% on either side of the mean. These groups
do not tend to adopt innovation for pleasure or increased status, but rather
for utility or necessity. The early majority tend to be willing to adopt an
innovation once they are convinced of its value, although they will seldom lead
in the adoption movement. The late majority require much more convincing or
peer pressure, and may adopt an innovation only when they feel it is necessary.
The final category, laggards, represent
the remaining 16% of the adoption population, more than one standard deviation
from the mean. They tend to support traditional technology and values, and may
be suspicious of change. They may never adopt and innovation, or may adopt it
only when forced to do so. RogersÕs Individual Innovativeness theory (Rogers,
2003) states that individuals that are predisposed to being innovative Ð that
is, individuals with a higher degree of innovativeness Ð will adopt an
innovation earlier than those who are less predisposed to innovativeness.
Another
factor that affects the adoption of innovation is the characteristics of
innovations, as perceived by individuals. RogersÕs Theory of Perceived
Attributes (Rogers, 2003) states that potential adopters base their adoption
decisions on five basic characteristics common to any innovation: relative
advantage, compatibility, complexity, trialability, and observability. These
characteristics are described as follows:
- Relative advantage Ð This refers to
the benefits that the individual or institution will derive from the
innovation over the existing situation. This does not have to be an
objective measure. It only matters that the innovation be perceived as
advantageous. The greater the perceived relative advantage, the faster
the adoption of the innovation.
- Compatibility Ð This refers to how
consistent the innovation is with existing values and norms of the
individual or institution. The more compatible the innovation is, the
faster it will be adopted.
- Complexity Ð This refers to the
degree to which the innovation is perceived as difficult to understand and
use. Innovations that have a more intuitive design and are simpler to
understand are adopted more rapidly than those that are harder to
understand.
- Trialability Ð This refers to the
degree to which an innovation may be experimented with on a limited basis.
New ideas that can be tried out on a partial basis will be adopted more
rapidly than those that canÕt.
- Observability Ð This refers to the
degree to which the results of an innovation are visible to others. The
easier it is for individuals to see the results of the innovation, the
more rapidly the innovation will be adopted.
Innovations
that are perceived by potential adopters as having greater relative advantage,
compatibility, trialability, and observability, and lower complexity, are more
likely to be adopted, and are more likely to be adopted quickly than
innovations without this mix of qualities.
When an innovation is adopted by an
individual, it may not be used in the precise manner that was originally
intended. Rather, the adopter might modify the innovation, or use it for a
different purpose or in a different manner than that envisioned by those
promoting its use. This phenomenon is called re-invention.
Innovation and Educational Technology
RogersÕs
classification of adopters has been adapted to support studies in many areas.
Geoghegan (1994) provides a good description of the adopter categories as used
to describe the adoption of educational technology:
- Innovators: This group includes the
"techies:" experimentalists who latch onto new technology as
soon as it appears. Their interest lies more with the technology itself
than with its application to significant problems. They know the details
of all the new hardware and software; and they are a significant resource
for vendors who need to test a new product. Innovators are often broadly
connected, and they form communities of shared interest that span both
disciplines and institutions.
- Early Adopters: These
"visionaries" blend an interest in technology with a concern for
significant professional problems and tasks. They look for the
breakthroughs in instructional methods or learning effectiveness that new
applications of technology enable. They explore new technologies for their
potential to bring about major improvements through qualitative,
discontinuous change. They are risk-takers and are not averse to
occasional failure. They often favor a tightly focused project orientation
in their work. Like the innovators, they are broadly connected within the
academic community, with good links to "innovators," and with
strong cross-disciplinary interests and ties. They are often quite
self-sufficient from a technical standpoint, either through their own
skills or through resources mustered through personal networks. Early
adopters are opinion leaders, respected by their peers, who look to them
for leadership in adopting new technologies.
- Early Majority: These are the
"pragmatists" who, although fairly comfortable with technology
in general, focus more on the concrete professional problems of teaching
and research than on the tools (technological or otherwise) that might be
used to address them. They adopt a "wait-and-see" attitude
toward new applications of technology and want solid references and
examples of close-to-home successes before adopting. They are not interested
in abrupt, discontinuous change, but are more attuned to evolutionary
modification of existing processes and methods. They want to see
compelling value in an innovation before adopting it. As a group the
mainstream is more risk averse than the innovators and early adopters.
Their networks are predominantly vertical, focused within the home
discipline or discipline area. The early majority tend to adopt an
innovation a little earlier than the average adopter.
- Late Majority: This is the
conservative or "skeptical" latter half of the mainstream. They
are similar in many respects to the early majority, though typically less
comfortable with technology. By definition, they accept innovation late in
the game, once the change has already become well established among the
majority. The late majority tend to adopt an innovation a little later
than the average adopter. Their level of risk aversion is high and their
communication networks are vertically oriented; that is, focused within
the home discipline area.
- Laggards: The last 16% of the
potential adopter population is the most likely never to adopt at all.
Their point of reference is the past and they base decisions on what has
been done previously. In teaching they are unlikely to employ educational
technology.
Expanding
upon RogersÕ ideas, Moore (1999), in the first printing of his book in 1991
described a potential obstacle in the innovation diffusion process, which can
occur as the innovation is passing from adoption by the early adopters to
adoption by the late majority. He termed this point Òthe chasm.Ó This is a
point at which the diffusion process becomes stalled in its progress.
Geoghegan (1994) applies this concept to
educational technology, where he considers it to be a significant potential obstacle
to the adoption of new educational technologies. That is, the adoption of a
new educational technology may have a tendency to stall once the early adopters
have begun using it. Geoghegan stresses that those in the academic community
who wish to see a new technology adopted must address this issue.
He describes a number of reasons that
institutions have problems bridging the gap. One is failure to recognize the
mainstream faculty as a distinct group within the community, and thus to
alienate them by trying to Òbring them aroundÓ to the early adopter mentality
rather than respecting their differences from that group.
Geoghegan (1994) contends that the
mainstream faculty need to be ÒsoldÓ on the new technology on their own terms.
They need to recognize for themselves the value of the technology. They also
want assurance that the institution is solidly committed to the introduction of
the innovation.
Teaching Styles
According
to Grasha (1996), instructors tend to select teaching processes because the
structural features associated with them are personally attractive. These
methods may be grounded in empirical research, but that is not what causes the
instructors to use them. Teachers are often unaware of the theory behind the
teaching methods that they choose. For example, they may use PowerPoint slides
because they are easy to create or because they personally enjoy using the
software, not knowing that by using them they are helping students with a
visual learning style to process information better. In addition, PowerPoint
slides usually highlight important concepts, enabling students to grasp them
more easily. Such practices as the use of PowerPoint slides reflect an
instructorÕs teaching style.
In 1988, Grasha began work on a
conceptual model of teaching style (Grasha, 1994), which resulted in the
Grasha-Riechmann teaching styles survey that is in wide use today. Grasha
developed this new instrument because he felt that current typologies focused
more on descriptive rather than functional attributes, and so provided no
guidance for modifying teaching styles when it was deemed appropriate. In
GrashaÕs view, a teaching style Òrepresented a pattern of needs, beliefs, and
behaviors that faculty displayed in the classroomÓ (p. 12). This style was
Òmultidimensional and affected how people presented information, interacted
with students, managed classroom tasks, supervised coursework, socialized
students to the field, and mentored studentsÓ (p. 12).
GrashaÕs
research led him to identify five teaching styles that were commonly found in
higher education. These styles are described below (Grasha, 1994):
- Expert: Possesses knowledge and
expertise that students need. Strives to maintain status as an expert
among students by displaying detailed knowledge and by challenging
students to enhance their competence. Concerned with transmitting
information and ensuring that students are well prepared.
- Formal Authority: Possesses status
among students because of knowledge and role as a faculty member. Concerned
with providing positive and negative feedback, establishing learning
goals, expectations, and rules of conduct for students. Concerned with
the Òcorrect, acceptable, and standard ways to do thingsÓ (p. 154), and
with providing students with the structure that they need in order to
learn.
- Personal model: Believes in
Òteaching by personal exampleÓ (p. 154) and establishes a prototype for
how to think and behave. Oversees, guides, and directs by showing how to
do things, and encouraging students to observe and then to emulate the
instructorÕs approach.
- Facilitator: Emphasizes the
personal nature of teacher-student interactions. Guides students by
asking questions, exploring options, suggesting alternatives, and
encouraging them to develop criteria to make informed choices. Overall
goal is to develop in students the capacity for independent action and
responsibility. Works with students on projects in a consultative fashion
and tries to provide as much support and encouragement as possible.
- Delegator: Concerned with
developing studentsÕ capacity to function autonomously. Students work
independently on projects or as part of autonomous teams. The teacher is
available at the request of students as a resource person.
Each
of the five categories has advantages and disadvantages for teaching (Grasha,
1994, 2002):
- Expert: Advantages: The
information, knowledge, and skills such individuals possess.
Disadvantages: If overused, the amount of information, knowledge, and
skills can be intimidating to less experienced students. May not always
explain the underlying thought processes that produced the answers.
- Formal authority: Advantages: The
focus on clear expectations and acceptable ways of doing things.
Disadvantages: A strong investment in this style can lead to rigid,
standardized, and less flexible ways of course delivery. May overlook
individual differences in student needs and goals as learners.
- Personal Model: Advantages: The
hands-on nature of the approach; and emphasis on direct observation and
showing people how to follow a role model. Disadvantages: some teachers
may believe their way is the best or only way to do things. Attempts are
made to ÒcloneÓ students into their own image or to avoid showing students
the range of options available to them. Some students may feel inadequate
if they cannot live up to the expectations and standards of the model.
- Facilitator: Advantages: The
personal flexibility, focus on student needs and goals, and the
willingness to explore options and alternative courses of action.
Disadvantages: This style is time consuming and is sometimes employed
when a more direct approach is needed. It can make learners uncomfortable
if it is not employed in a positive and affirming manner.
- Delegator: Advantages: Contributes
to studentsÕ learning that they have skills and knowledge that they can
independently employ. The knowledge that students gain about their
skills, and that someone in authority trusts them to think and act
competently with a minimum level of supervision contributes to their
vision of themselves as becoming professionals in the field.
Disadvantages: The level and ability of learners may be misjudged and
students may not possess the ability to think and behave in a more
autonomous manner. Some learners may become anxious about not having
closer supervision or may not know how to interact with a faculty member
who functions as a consultant and resource person.
Grasha
believed that instructors should not be pigeonholed into one of the five
categories. Rather, he felt that instructors possess traits of each of the
categories, although to varying degrees. Thus, he designed his teaching styles
inventory to yield a measure for each category, reflecting the degree to which
the instructor possesses the qualities for that category. Each of the five
scores ranges from 1 to 7, where 1 is low and 7 is high. That is, a score of 1
indicates a style whose characteristics are unimportant to the instructorÕs
approach to teaching and a score of 7 represents a style whose characteristics
are very important to the instructorÕs approach to teaching.
The
mix of teaching styles that an instructor possesses exerts a great influence on
the atmosphere of his or her classroom. For instance, instructors who exhibit
more expert and formal authority traits send the message to students that ÒIÕm
in charge here.Ó This creates a rather neutral or ÒcoolÓ climate (Grasha,
1994). Instructors who exhibit more facilitator and delegator traits sent the
message to students that ÒIÕm here to consult with you on the projects and
issues you are exploringÓ (p. 13). This is more likely to result in a warmer
emotional climate in the classroom (Grasha).
Teaching Style and Technology Use
Teaching
styles are associated with theoretical and philosophical issues about
knowledge. Whether the instructor is aware of them or not, these conceptual
matters underlie his or her teaching. The manner in which technology is used
is often indicative of an underlying teaching style. The technology may either
reinforce the instructorÕs preferred teaching style, if it is in line with that
style, or create pressures for him or her to modify that style. Grasha and
Yangarber-Hicks (2000) studied teaching styles and their relation to the use of
technology in teaching in a recent study. Their results showed that teaching
styles remain fairly constant, whether the class is taught in a traditional
manner or with the use of technology.
Using GrashaÕs five teaching styles,
Grasha and Yangarber-Hicks (2000) formed clusters based on research (Grasha,
1994, 1996), which show the four most prevalent combinations of teaching styles
that instructors use, and described appropriate technology use for instructors
in that cluster. In each example, the most prevalent teaching styles are
listed in order, although the others of the five teaching styles will be
present to a lesser degree.
- Expert/Formal Authority Ð
Instructors whose teaching styles are represented by this cluster will
appreciate the transmission capabilities of technology. They must be
careful, however, to involve students in the material and establish a
two-way dialogue in order to keep the students active rather than passive
receivers of information. This can be accomplished by having them answer
questions, challenge what they hear and see in writing, or contribute
their own thoughts about the topic.
- Personal Model/Expert/Formal
Authority Ð This cluster represents the teaching styles of instructors who
like to coach, guide, and model how to do things. These functions are
well suited to technology. Instructors can use technology to demonstrate
or model what is being taught. For example, after viewing a demonstration
on a medical procedure by an expert, learners can be coached in what to do
by the instructor and receive immediate feedback on their efforts.
- Facilitator/Personal Model/Expert Ð
Instructors represented by this cluster can use the technology to create
activities that encourage active learning. They remain in the picture
clarifying information, answering questions and overseeing what the
students are doing. Learners are actively engaged in their own learning
and in that of their peers. The instructor helps them to find ways to
explore information and acquire skills. This cluster includes such strategies
as case studies, research projects, problem-based learning assignments,
small-group discussions, role-playing, and simulations. Students actively
participate and collaborate in these learning activities.
- Delegator/Facilitator/Expert Ð This
cluster represents the teaching styles of instructors who prefer to act as
consultants and resource people to students that are working alone or in
small groups, providing help when needed. Students shape and direct their
own learning tasks. An online literature search is an example of a
learning activity that might be used by an instructor represented by this
cluster of teaching styles.
Teaching Style and Technology Adoption
In
recent years, researchers have studied GrashaÕs work in relation to the
adoption of educational technology. One example, Zisow (2000) claims that Òthe
key in adapting new technologies lies in teacher style, not in technologyÓ (p.
36). She feels that although the available technological tools change over
time, resistance to change is constant, reflecting fear of change and
innovation. Those instructors whose teaching styles are centered on
innovative, constructivist techniques may be more comfortable adopting new
technologies.
For those who do adopt new technologies,
Grasha and Yangarber-Hicks (2000) caution against choosing methods of use
merely because they are personally attractive. These instructors should
instead focus on the conceptual concepts underlying the methods. Knowledge of
their own teaching style preferences would also be helpful, by helping
instructors to make informed choices among alternative ways to teach, and to
identify parts of their teaching styles that are either helpful or problematic
with regard to their use of technology in support of their teaching (Grasha,
2002).
Summary
Course
management systems offer many potential benefits to faculty and students. They
are commonly used in support of distance education, but can also enhance
face-to-face courses. Course management systems have also facilitated the creation
of a new type of course, a hybrid
or blended course,
that combines the best features of face-to-face and distance courses.
Course
management systems are valued for their organizational tools. These tools
enable the instructor to manage course content, assignments, schedule,
materials, and resources. They can be especially helpful in support of
large-enrollment classes.
Course
management systems are valued for their communication tools. These tools
enable the instructor to provide timely feedback. They also allow students to
communicate with each other at any time of day or night. The lack of
face-to-face contact is preferred by some students, who might otherwise be shy
about participating in a class discussion.
Hybrid
courses encourage student engagement with the course materials. Many
instructors and students feel that hybrid courses increase student learning.
Hybrid courses facilitate learning that is meaningful and deep.
When
a new educational technology, such as a course management system, is offered to
instructors, their decision to adopt or not adopt the technology might be based
on a number of factors. Among them are the type of innovation-decision that
was made by the institution, the degree of innovativeness of the instructor,
and the teaching style of the instructor.
There
are three types of innovation-decisions, as defined by Rogers (2003). The
first is an optional innovation-decision, where the individual chooses whether
to adopt or not adopt the new technology independent of whether others adopt
it. The second is a collective innovation-decision, where the members of a
group reach a consensus regarding whether or not to adopt the technology. The
third is an authority innovation-decision, where a governing board or other
authority makes the decision to adopt a new technology and then mandates its
use by the faculty.
The
degree of innovativeness, as reflected in the adopter category, of the
instructor may have an influence on how quickly an instructor adopts a new
technology, and perhaps on whether he or she adopts it at all. According to
Rogers (2003), the most likely to adopt quickly are a small group, representing
only 2.5% of the adopters, known as the innovators. They are quickly followed
by the early adopters. Later come the early majority and late majority. Much
later, or never, come the laggards.
The
teaching style of the instructor can also influence his or her decision to
adopt a new educational technology, as well as the way he or she uses that
technology in support of a course. Grasha (1996) defined five teaching styles
that all instructors will possess to some degree. They are: expert, formal
authority, personal model, facilitator, and delegator.
Both
RogersÕs diffusion of innovation
research and GrashaÕs teaching styles research were used as the basis for this study of the
characteristics of adopters and nonadopters of a course management system.
Chapter 3
METHOD OF RESEARCH
This study represents a survey research
design. The survey group (n
= 62) consisted of faculty members at Rose-Hulman Institute of Technology. A
purposive sample of 31 adopters and 31 nonadopters of ANGEL was taken from the
faculty. A personal interview, lasting approximately ten minutes, was
conducted with each subject. During the interview, the subject was first asked
to determine his or her teaching style scores by completing the
Grasha-Riechmann Teaching Styles Inventory shown in Appendix A. This survey
was administered first because it took the longest to complete Ð approximately
three to four minutes Ð of the instruments. The subject was then asked to
identify his or her Adopter Category Index by means of the Adopter Category
Rubric shown in Appendix A. The subject was asked to complete the ANGEL Use
Checklist shown in Appendix A. The subject was asked his or her reasons for
adoption or nonadoption of ANGEL. One of the adopters declined to complete the
interview process, resulting in a survey group size of n = 61 rather than n = 62. This was comprised of 30 adopters
(n = 30) and 31
nonadopters (n = 31).
Research Questions and Hypotheses
The
research questions for this study were:
- What are the differences in adopter
characteristics and teaching styles of adopters and nonadopters of the
ANGEL course management system?
- What are the reasons for adoption
and nonadoption of the ANGEL course management system?
- How do adopters use the ANGEL course
management system?
The
first research question Ð ÒWhat are the differences in adopter characteristics
and teaching styles of adopters and nonadopters of the ANGEL course management
system?Ó Ð was addressed using data from the Adopter Category Rubric and the
Grasha-Riechmann Teaching Styles Inventory, both shown in Appendix A. The
adopter characteristics of the faculty were determined by means of the Adopter
Category Rubric. This yielded an adopter category index that was a measure of
the degree of innovativeness of the subject. The teaching styles of the
faculty were determined by means of the Grasha-Riechmann Teaching Styles Inventory,
which yielded a measure for each of five teaching styles: expert, formal
authority, personal model, facilitator, and delegator.
The null hypothesis for the quantitative
analysis of the adopter and teaching styles data was:
- The adopter category index for ANGEL
adopters will be the same as or higher than the adopter category index for
ANGEL nonadopters.
The
adopter category is inversely related to the degree of innovativeness of an
individual. That is, an individual with a low adopter category index has a
high degree of innovativeness, while an individual with a high adopter category
index has a low degree of innovativeness. Restating Hypothesis 1 in terms of
degree of innovativeness would yield the following equivalent null hypothesis:
ANGEL adopters will have the same or a
lower degree of innovativeness than ANGEL nonadopters.
The
remaining null hypotheses are:
- ANGEL adopters will score no
differently in the ÒexpertÓ category for teaching style than ANGEL
nonadopters.
- ANGEL adopters will score no differently
in the Òformal authorityÓ category for teaching style than ANGEL
nonadopters.
- ANGEL adopters will score no
differently in the Òpersonal modelÓ category for teaching style than ANGEL
nonadopters.
- ANGEL adopters will score no
differently in the ÒfacilitatorÓ category for teaching style than ANGEL
nonadopters.
- ANGEL adopters will score no
differently in the ÒdelegatorÓ category for teaching style than ANGEL
nonadopters.
The
second research question Ð ÒWhat are the reasons for adoption and nonadoption
of the ANGEL course management system?Ó was addressed by asking each subject to
explain his or her reason for adopting or not adopting ANGEL when it was
offered to them.
The third research question Ð ÒHow do
adopters use the ANGEL course management system?Ó Ð was addressed by means of
the ANGEL Use Checklist, shown in Appendix A. The information gathered from
these sources is analyzed in Chapter 4: Results and Chapter 5: Discussion,
Conclusions, and Recommendations.
Sampling Procedure
The
subjects for this study were taken from the full-time faculty at Rose-Hulman
Institute of Technology. Rose-Hulman employs 150 full-time faculty, who teach
a wide range of courses in engineering, science, mathematics, and the
humanities. Ninety-eight percent have earned PhDs. Faculty are selected on
their ability to teach and on their area of expertise.
A list of
Rose-Hulman faculty was sorted into random order using an Excel spreadsheet.
Faculty members were contacted in that order, and were questioned briefly to determine
whether they were adopters or nonadopters, as defined for this study. The
first 31 ANGEL adopters and the first 31 ANGEL nonadopters that were identified
were asked if they would participate. The reason for selecting 31 in each
category is that an extra adopter and nonadopter were scheduled for interview,
in case any of the scheduled interviews were not completed as planned. There
were more adopters than nonadopters on the faculty, so it was necessary to go
further down the list than the first 62 faculty members in order to contact the
first 31 nonadopters.
An
interview time was scheduled for each of the 62 subjects. None of the
potential subjects declined to schedule an interview, so it was not necessary
to replace any potential subjects with others further down the list. However,
one of the scheduled subjects declined to complete the interview process.
Thus, results were obtained for 30 adopters and 31 nonadopters. Each subject
was asked to complete survey instruments, described below, and to answer a
follow-up question, during a brief (approximately 10 minute) interview.
Survey Instruments
For
the purposes of this study, there were three instruments, which are shown in
Appendix A. The Adopter Category Rubric is a rubric designed for a subject to
self-select his or her adopter category index, along a continuum which spans
the categories of innovator, early adopter, early majority, late majority, and
laggard. It is a subjective measure representing the subjectÕs perception of
his or her adopter category. It is intended to yield a measure of the
subjectÕs degree of innovativeness, along a continuum from highest to lowest
degree.
The Grasha-Riechmann Teaching Styles
Inventory (GRTSI) is a survey instrument used to determine teaching style. Dr.
Grasha introduced the GRTSI in 1996 in his book, Teaching with Style: A
Practical Guide to Enhancing Learning by Understanding Teaching and Learning
Styles. The GRTSI uses
a survey instrument with 40 items, and uses a 7-point Likert scale for each.
Each item asks for a response to a statement about teaching style or attitude.
For example: ÒFacts, concepts, and principles are the most important things
that students should acquireÓ and ÒMy teaching goals and methods address a
variety of student learning stylesÓ.
The survey responses were used to
determine five teaching styles scores: expert, formal authority, personal
model, facilitator, and delegator. Each score ranges from 1 to 7, with 1
indicating a low preference for that style and 7 indicating a high preference
for that style. A reliability analysis was performed on the instrument. The
results of that analysis are included in Chapter 4: Results.
The ANGEL Use Checklist is a checklist of
the features of the ANGEL course management system. The subject was asked to
check the box by every feature that he or she has used in a course at
Rose-Hulman.
Each subject was asked the follow up
question:
ÒWhen ANGEL was offered just before the
start of this academic year, why did you choose to adopt [not adopt] it?Ó
Materials and Equipment
ANGEL
Learning Management System is a course management system that supports
organization of course materials, management of communication, and development
of collaborative learning experiences. Rose-Hulman uses version 6.2 of the
ANGEL software.
The
ANGEL course management system runs on two HP Proliant DL 580 servers. Each
contains an Intel Xeon 3 GHz processor, running Windows 2003 Server software.
One of the servers employs Microsoft SQL Server software to manage the underlying
database. The other acts as a web server for the ANGEL user interface and
employs Microsoft Internet Information Server (IIS).
Survey Procedure
A
purposive sample of 31 adopters and 31 nonadopters was drawn from a randomly
sorted list of the faculty at Rose-Hulman Institute of Technology in Terre
Haute, Indiana. Data were collected from 30 of the adopters and from all of the
nonadopters during brief (approximately 10 minute long) interviews, using the
instruments shown in Appendix A. One of the adopters declined to complete the
interview process.
Design
This
study employed a randomized post-test-only survey design. A strength of the
design is the random selection of subjects for the survey. The design is a
post-test-only survey design, in that the survey instruments were administered
after the fact; that is, the faculty members surveyed had already made the
decision to adopt or not adopt the ANGEL course management system and had
already chosen to use various features of ANGEL.
Statistical Analysis
For
each subject, the data from the Grasha-Riechmann Teaching Styles Inventory was
analyzed to determine the five teaching style scores. These, along with the
results from the Adopter Category Rubric, were entered into SPSS 12.0 for
statistical analysis. An alpha level of .05 was used as a level of confidence
for all statistical tests. This level provides a balance between the
possibility of Type I and Type II errors, and is commonly used in analysis of
this type.
To determine whether adopters and nonadopters
differ in their teaching styles, a series of two-tailed Independent Samples t-tests was run. The independent
variable, adoption/nonadoption, was used to create the two samples. One sample
contained the adopters and the other contained the nonadopters. For each t-test, the dependent variable was a
measure of teaching style. The five t-tests each had as dependent variable one of the measures
for: expert, formal authority, personal model, facilitator, and delegator.
These measures were calculated for each individual subject from the
Grasha-Riechmann Teaching Styles Inventory. Results were analyzed for
significance at the .05 level, but the actual significance levels are contained
in this report.
To determine whether adopters and
nonadopters differ in their adopter categories Ð that is, in their degree of
innovativeness Ð a one-tailed Independent Sample t-test was run. As in the teaching style
analysis, the independent variable, adoption/nonadoption, was used to create
the two samples. One sample contained the adopters and the other contained the
nonadopters. The dependent variable was the adopter category index, a measure
of degree of innovativeness, which was obtained for each individual from the
Adopter Category Rubric. Results were analyzed for significance at the .05
level, but the actual significance levels are contained in this report.
Chapter 4
RESULTS
Adopter Category
According
to Rogers (2003), when a new technology is available to a group, it will be
adopted first by the innovators, followed in turn by the early adopters, early
majority, and late majority. The remaining members of the group, the laggards,
may or may not adopt it at all. Rogers also found that each adopter category
consists of individuals with a similar degree of innovativeness, with the
innovators having the highest degree of innovativeness, and the laggards having
the lowest degree of innovativeness.
In line with RogersÕs theory, when the
ANGEL course management system was made available to the faculty of Rose-Hulman
just before the 2004/2005 school year, its adoption by faculty could be
expected to follow that pattern. One result would be that one would expect to
find more innovators and early adopters in the sample of those who adopted
ANGEL than in the sample of those who did not adopt ANGEL.
The
Adopter Category Rubric was designed to determine the adoption group into which
a faculty member fit. Thus, it provided an indication of the degree of
innovativeness of the faculty member. Table 4.1 shows the rubric along with an
indication of the adopter category that corresponds to each index.
The
index number is inversely proportional to the degree of innovativeness of the
individual. That is, those with a lower adopter category index, such as
innovators and early adopters, exhibit a higher degree of innovativeness than
those with a higher adopter category index, such as late majority and laggards.
During the study, each faculty
participant was asked to determine his or her adopter category using the
Adopter Category Rubric. The results were analyzed to determine whether the
following null hypothesis could be rejected:
H0: The adopter category
index for ANGEL adopters will be the same or higher than the adopter category
index for ANGEL nonadopters.
Stated in terms of innovativeness, the
null hypothesis would be:
H0: ANGEL adopters will have
the same or a lower degree of innovativeness than ANGEL nonadopters.
A
one-tail t-test was
conducted on the study data. The independent variable was
adoption/nonadoption. This divided the subjects into two groups Ð one of
adopters (n = 30) and
one of nonadopters (n
= 31). The dependent variable was the adopter category index. The results are
shown in table 4.5.
The results showed a 1-tailed
significance value of .15, which was higher than the alpha level of .05.
Thus, although the average adopter index was lower for the sample of faculty
adopters than for the sample of faculty nonadopters, and there were more early
adopters in the sample of faculty adopters than in the sample of faculty
nonadopters, these differences were not statistically significant. This also
indicates that ANGEL adopters exhibited a slightly higher degree of
innovativeness than ANGEL nonadopters; but, again, the difference was not
statistically significant.
Teaching Style
During the study, each faculty
participant was asked to complete the Grasha-Riechmann Teaching Styles
Inventory. This is a survey instrument with 40 items, which uses a 7-point
Likert scale for each. Each item asks for a response to a statement about
teaching style or attitude. The survey responses are used to determine five
teaching styles scores: expert, formal authority, personal model, facilitator,
and delegator. Each score ranges from 1 to 7, with 1 indicating a low
preference for that style and 7 indicating a high preference for that style.
The results of the Teaching Styles Inventory were analyzed to determine whether
the following null hypotheses could be rejected:
H0: ANGEL adopters will score
no differently in the ÒexpertÓ category for teaching style than ANGEL
nonadopters.
H0: ANGEL adopters will score
no differently in the Òformal authorityÓ category for teaching style than ANGEL
nonadopters.
H0: ANGEL adopters will score
no differently in the Òpersonal modelÓ category for teaching style than ANGEL
nonadopters.
H0: ANGEL adopters will score
no differently in the ÒfacilitatorÓ category for teaching style than ANGEL
nonadopters.
H0: ANGEL adopters will score
no differently in the ÒdelegatorÓ category for teaching style than ANGEL
nonadopters.
Table 4.8 shows the distribution of
faculty in the adopter and nonadopter samples, by level (low, moderate, high)
within each teaching style. The breakdown is shown separately for adopters and
for nonadopters. As the table illustrates, the teaching styles of both groups
were quite similar.
Range of
Teaching Styles Scores
Teaching Style Levels for Adopters and
Nonadopters
A series of two-tailed t-tests was conducted on the study data.
The independent variable for each test was ANGEL adoption/nonadoption. This
divided the data into two groups Ð one of adopters and one of nonadopters. The
dependent variable for each t-test
was one of the five teaching style measures that were obtained from the
Grasha-Riechmann Teaching Styles Inventory. These were the measures for
Expert, Formal Authority, Personal Model, Facilitator, and Delegator. The
results for the five t-tests
are shown in Table 4.9.
The
results for all of the t-tests
showed two-tailed significance values that were greater than the alpha level of
.05. This indicated that there were no significant differences in teaching
style between the faculty in the sample of ANGEL adopters and the faculty in
the sample of ANGEL nonadopters.
Reliability Analysis
Reliability
analyses were performed for the Grasha-Riechmann Teaching Styles Inventory.
The instrument consists of forty questions, which measure five teaching styles.
Each of the forty questions is designed to measure one of the five styles.
The questions that measure each of the five styles are shown in Table 4.10:
Questions Related to Teaching Styles
A
ChronbachÕs Alpha coefficient was calculated for the entire teaching styles
inventory, and for the group of questions related to each of the five styles.
The results are shown in Table 4.11 below. The Chronbach's Alpha is a measure
of the internal consistency of the items, representing the average correlation
of all possible situations where half of the items are correlated with the
other half of the items. An alpha value of .70 or higher is considered
acceptable. As the results indicate, the overall reliability is in the
acceptable range, although some of the individual question groups do not score
as well.
Reliability Analysis Results
Reasons for Adoption/Nonadoption
During
the study, each faculty participant was asked his or her reasons for adopting
or not adopting ANGEL. Among the nonadopters, the primary reason for nonadoption
Ð cited by 25 of the 31 subjects Ð was a lack of perceived value in adopting
ANGEL over their current system of course management. Most of these faculty
members Ð 19 of the 25 Ð maintained a course website that they were happy with
and saw no reason to switch to using the ANGEL course management system.
The
second most common reason for not adopting ANGEL was a reluctance to devote the
time necessary for the learning curve. Fifteen of the nonadopters cited this
as a reason for their nonadoption. It is interesting to note that none of the
15 categorized themselves as early adopters. Rather, they were members of the
early majority or late majority, groups known for being more cautious about
adopting new technologies.
Three of the nonadopters didnÕt like the
features of ANGEL, or felt that it did not allow them to do what they wanted to
do.
Among
the adopters, the most common reason for adopting ANGEL Ð cited by 20 of the 30
adopters Ð was that they felt it would be better than their current method of
course management. Fifteen of the 20 decided to adopt ANGEL primarily for its
organizational capabilities. These faculty adopters liked the ability to have
all course materials in a central location and appreciated the ease of updating
and maintaining those materials.
The
second most commonly given reason for adoption Ð cited by 11 of the 30 adopters
Ð was that they liked some of the features that are unique to ANGEL. These
features allow them to do things that they either could not do before, or could
do much more easily with ANGEL.
The
third common reason for adopting ANGEL was curiosity about trying something
new. This reason was given by 9 of the adopters, nearly all of whom were in
RogersÕs early adopter category. A common trait of people in this category is
a willingness to take a risk on adopting a new technology.
Five
faculty adopters cited an appreciation of ANGELÕs communication capabilities.
Three of the faculty adopters felt somewhat obligated to adopt ANGEL, to
support the instituteÕs decision to offer the course management system.
Another reason for adoption, given by four of the faculty adopters, was that
they thought that it would benefit their students.
Adoption Decisions
RogersÕs
Theory of Perceived Attributes (2003) states that potential adopters base their
adoption decisions on five basic characteristics common to any innovation:
relative advantage, compatibility, complexity, trialability, and observability.
These characteristics are described briefly below:
- Relative advantage: the benefits
that the individual believes he or she will derive from the innovation
over the existing situation.
- Compatibility: how consistent the
innovation is with the individualÕs existing values and norms.
- Complexity: the degree to which the
innovation is perceived as difficult to understand and use.
- Trialability: the degree to which an
innovation may be experimented with on a limited basis.
- Observability: the degree to which
the results of an innovation are visible to others.
Innovations
that are perceived by potential adopters as having greater relative advantage,
compatibility, trialability, and observability, and lower complexity, are more
likely to be adopted, and are more likely to be adopted more quickly than
innovations without this mix of qualities.
Relative
advantage was the primary factor affecting the decision to adopt or not adopt
the ANGEL course management system. Two thirds of the adopters Ð 20 out of 30
Ð felt that there would be more benefit in adopting ANGEL over not adopting.
However, an even greater number of nonadopters Ð 25 out of 31 Ð felt that there
was no added advantage to adopting ANGEL.
The
second major factor that influenced the adoption decision was complexity. The
perception of the ease/difficulty of adopting ANGEL or the ease/difficulty of
using ANGEL had a primarily negative impact on ANGEL adoption decisions.
Nearly two thirds of the nonadopters Ð 20 out of 31 Ð felt that ANGEL was too
difficult to use, or that mastering ANGEL would require too steep a learning
curve. Only 3 of the adopters mentioned ease of use as a factor affecting
their decision to adopt.
Trialability
was cited as an advantage by two of the adopters, who decided to adopt ANGEL in
part because they could try it out on just one of their courses, while
continuing to manage their other courses in their usual way. However, many
adopters took advantage of the trialability of a course management system by
using ANGEL only for some of their course features. Information on the
features used by ANGEL adopters is included later in this chapter.
A
perceived lack of compatibility with oneÕs beliefs was cited by three faculty
members as a factor in the decision to not adopt ANGEL. In these cases, the
subject felt that either that the standardization or the communication features
made ANGEL too impersonal.
There
were three cases where observability influenced an adoption decision. The
first was cited by a faculty member who decided to adopt ANGEL in part because
of the experiences of one of the faculty members who was instrumental in
bringing ANGEL to Rose-Hulman and who was deeply involved in its use. The
other two were team teaching a course with other faculty members, and adopted
ANGEL in order to be consistent with the others.
ANGEL Use
The
most commonly used ANGEL features, used by nearly all of the adopters surveyed,
were organizational features: syllabus, course schedule, assignments, and
materials and resources. In fact, 20 of the 30 adopters used all four of those
features.
The least used features were whatÕs new
and chat rooms. Features listed in the ÒotherÓ category included picture book,
Òwho done it?Ó, and logged activity report. Two faculty subjects mentioned the
use of regular ANGEL features in a different manner to support teamwork.
Figure
4.2 shows the number of features used by the ANGEL adopters. The number of
features with the highest frequency was nine, with six of the ANGEL adopters
using nine features in their course management. However, there was a wide
range of ANGEL use among the adopters, with usages ranging from two to fourteen
features.
The following chapter expands upon the
results presented in this chapter. It includes a more detailed discussion of
the results, plus recommendations based on the findings of the study.
Chapter 5
DISCUSSION AND RECOMMENDATIONS
ANGEL Adoption
According to RogersÕs definitions of
innovation-decision types (2003), the choice by faculty to adopt or not adopt
the ANGEL course management system at Rose-Hulman was an optional
innovation-decision. That is, the choice was made by the individual faculty
member, independent of the decisions of other faculty members.
It
is helpful to relate the adoption and nonadoption of ANGEL at Rose-Hulman to
the five basic characteristics of innovations, as outlined by Rogers (2003).
These five basic attributes, common to any innovation, are: relative advantage,
compatibility, complexity, trialability, and observability. Past research
indicates that these five qualities are the most important characteristics of
innovation in explaining the rate of adoption. The following discussion relates
the experience at Rose-Hulman to RogersÕs Theory of Perceived Attributes.
Relative
advantage refers to the benefits that the individual will derive from the
innovation over the existing situation. This does not have to be an objective
measure. It only matters that the innovation be perceived as advantageous.
The greater the perceived relative advantage, the faster the adoption of the
innovation. Diffusion scholars have found relative advantage to be one of the
strongest predictors of an innovationÕs rate of adoption (Rogers, 2003).
Indeed, at Rose-Hulman, this was the attribute most related to the decision to
adopt or not adopt. The faculty who adopted ANGEL perceived that there was a
relative advantage to doing so: for them, for their students, or for the
institute. They felt that there was an advantage in adopting ANGEL over their
current method of course management Ð in most cases, a course website. The
nonadopters did not perceive a relative advantage to adopting ANGEL. In fact,
the most commonly cited reason for not adopting ANGEL Ð cited by 25 of the 30
nonadopters Ð was that the faculty member could not see that it had any
advantage over their current website-based way of handling course management.
Compatibility refers to how consistent
the innovation is with existing values and norms of the individual. The more
compatible the innovation is, the faster it will be adopted. This was not a
big issue at Rose-Hulman, especially for the adopters. However, three of the
nonadopters did express concerns about the aspects of ANGEL that they felt were
incompatible with their values or preferences. One issue is the standardization
inherent in a course management system. There are those who feel that the
standardization of websites under ANGEL detracts from the personal feel of
those sites. These faculty members like to show students the personal side of
their professor, including information about and photos of the professor, as
well as links related to his or her work. They felt that ANGEL made it harder
to do this.
The second issue that was raised
regarding compatibility with values and practices had to do with the electronic
communication that is built into ANGEL. Two of the faculty adopters felt that
this was too impersonal, that in a small face-to-face class, all communication
should be face-to-face. They felt that ANGEL created a distance between them
and the students, rather than enhancing the closeness. These faculty members
prefer to have the student come to their office to talk, rather than use the
email, discussion board, or chat features of the ANGEL system.
None of the adopters or nonadopters
expressed a concern with ANGELÕs compatibility with their previous course
management tasks. All seemed to feel that ANGEL did many of the same things
that were done in their management of courses before, by other means.
Another attribute of innovations that
affects adoption is complexity. This refers to the degree to which the
innovation is perceived as difficult to understand and use. Innovations that
have a more intuitive design and are simpler to understand are adopted more
rapidly than those that are harder to understand. Rogers (2003) found this
feature to be less important to the rate of adoption than either relative
advantage or compatibility. The impact that the issue of complexity had at
Rose-Hulman was that a nearly two thirds of the nonadopters Ð 20 out of 31
nonadopters Ð felt that ANGEL was too difficult to use, or that adopting ANGEL
would require too much of a learning curve.
Trialability refers to the degree to
which an innovation may be experimented with on a limited basis. New ideas
that can be tried out on a partial basis will be adopted more rapidly than
those that canÕt. A course management system, with its many independent
features, is inherently trialable. This probably improved the rate of adoption
for ANGEL at Rose-Hulman. Indeed, a number of adopters did so at first on a
limited basis. There were two patterns of trialability observed: Some adopters
tried ANGEL out on only one of their courses, and used their traditional
management methods for their other courses.
Many adopters used ANGEL for only some
aspects of their course management, such as the syllabus or the assignment
schedule, and stayed with their traditional methods for other aspects. In
fact, thirteen of the adopters used fewer than half of the ANGEL features that
were available for their course support. Only two adopters used more than
three quarters of the features.
The
last of RogersÕs five basic attributes of innovations is observability. This
refers to the degree to which the results of an innovation are visible to
others. The easier it is for individuals to see the results of the innovation,
the more rapidly the innovation will be adopted. This attribute came into play
mainly for team-taught courses. Two faculty members adopted ANGEL in order to
be consistent with the others with whom they shared the teaching responsibility
for a course. A third example of observability was a faculty member who decided
to adopt ANGEL in part because of the experiences of one of the faculty members
who was instrumental in bringing ANGEL to Rose-Hulman and who was deeply
involved in its use. Other than those situations, the independent nature of
higher education instruction tends to limit the observability of an innovation
such as a course management system.
ANGEL Use
When an innovation is adopted by an
individual, it may not be used in the precise manner that was originally
intended. Rather, the adopter might modify the innovation, or use it for a
different purpose or in a different manner than that envisioned by those promoting
its use. This phenomenon is called re-invention. At Rose-Hulman, there seemed
to be some reinvention in the way that ANGEL was used to accommodate teamwork.
A further analysis of that reinvention is beyond the scope of this study.
Nearly all of the ANGEL adopters at
Rose-Hulman used the course management system for its organizational features.
Most (26 out of 30) also used it for its communication features. The primary
focus of all of the ANGEL adopters was the operational management of their
courses. ANGEL was not used to create a hybrid course. There was little
evidence of ANGEL being used to facilitate Òmeaningful learningÓ.
Recommendations
More than half (17
out of 31) of the adopters fit the classic early adopter profile, as defined by
Rogers (2003). Three of them said that they consider themselves leaders in the
adoption of technology, among the first to try out something new. According to
Rogers, early adopters function as opinion leaders, helping to convince others
to adopt an innovation. So it would be helpful to have a lot of early adopters
interested in adopting a new educational technology.
If an organization
wants a new educational technology
to be adopted, it must convince potential adopters that the technology has
value for them. Concentrating on the early adopters, convincing them that
there is an advantage in adopting the technology, would be a good strategy.
One way to do this would be through Òfast startÓ training that would get the
early adopters involved with the new technology quickly. As the early adopters
begin to embrace the new technology, others will tend to follow their lead.
Although the focus was more on support than on persuasion, Rose-Hulman followed
this strategy in creating an Òearly adopterÓ support group when ANGEL was first
being introduced.
An institution that is introducing a new
educational technology should heed the compatibility issues that were raised by
this study. These issues were concern over the standardization imposed by a
course management system, and a feeling that the communication features of
ANGEL made communication too impersonal.
The institution must address the issue of
standardization. It should help users to see how they can individualize and
personalize their sites within the confines of the course management system
structure. Faculty would thus have the best of both worlds, a personalized
course site that takes advantage of the many helpful features of the course
management system.
The other compatibility issue that was
raised is communication. Faculty should be shown how the course management
system could be used to enhance communication and to make students feel closer
and more comfortable with the instructor and with their classmates. The result
would be communication that is more personal, rather than more impersonal.
As was mentioned earlier, the primary use
of the ANGEL course management system at Rose-Hulman was for the organizational
and communication tasks involved in managing courses. There was little
evidence of its use for the promotion of meaningful learning. If an
institution wishes to promote the use of a course management system for hybrid
courses or to promote meaningful learning, then training should emphasize
pedagogy as well as the mechanics of operation of the system. Faculty members
should be taught how to redesign their courses in a hybrid manner, and how to
select the portions of their courses that would be more effectively implemented
online.
Teaching Style
The statistical analysis, described in
the previous chapter, of teaching styles for the sample group of ANGEL adopters
and that of nonadopters revealed no statistically significant difference
between the two groups. Each of the subjects completed the Grasha-Riechmann
Teaching Styles Inventory, a process. Although this information did not
provide any results that would help on an institutional level, the results
could be of use to faculty on a more personal basis.
As Grasha and Yangarber-Hicks (2000)
pointed out, teachers are often unaware of the theory behind the teaching
methods that they choose. For example, they may use PowerPoint slides because
they are easy to create or because they personally enjoy using the software,
not knowing that by using them they are helping students with a visual learning
style to process information better. In addition, PowerPoint slides usually
highlight important concepts, enabling students to grasp them more easily.
Faculty should be aware of the theory
behind their teaching practices, so that they could use them more effectively.
They should be aware of their teaching styles and the teaching methods that
best complement those styles, so that they could adopt some of those methods
and enhance their teaching effectiveness.
Recommendation
A recommendation of this study is to make
the Grasha-Riechmann Teaching Styles Inventory available to all faculty and to
provide materials that would enable faculty to improve their teaching
effectiveness based on their results. A website devoted to teaching style
should be created to serve as a repository for this information.
Future Research
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APPENDIXES
APPENDIX A
Survey Instruments
Grasha-Riechmann Teaching Styles
Inventory
ANGEL Use Checklist
Adopter
Category Rubric
Please
use the following rubric to determine your adopter category index. Choose the
number of the description that most reflects your approach to the adoption of
new technology; that is, technology in general, not just educational
technology.
|
I
tend to latch onto new technology as soon as it is available to me. My
interest lies more with the technology itself than with its application to
specific problems.
|
1
|
|
In
between 1 and 3
|
2
|
|
I
explore new technologies for their potential to bring about improvements. I
am willing to try new things, and am not averse to occasional failure.
|
3
|
|
In
between 3 and 5
|
4
|
|
I
adopt a Òwait and seeÓ attitude toward new technology, and want examples of
close-to-home successes before adopting. I want to see value in an
innovation before adopting it.
|
5
|
|
In
between 5 and 7
|
6
|
|
I
accept new technology later in the game, once the technology has become
established among the majority.
|
7
|
|
In
between 7 and 9
|
8
|
|
I
am usually not interested in adopting new technology.
|
9
|
Grasha-Riechmann
Teaching Styles Inventory
This material is
copyrighted by Alliance Publishers. It can be found in:
Grasha,
A. F. (1996). Teaching with style: A practical guide to enhancing learning
by understanding teaching and learning styles.
Pittsburgh, PA: Alliance Publishers.
Angel Use
Checklist
Please place an X in the box
beside each feature of ANGEL that you have used in a course that you have
taught or are teaching at Rose-Hulman. If you have not used ANGEL at all at
Rose-Hulman, please check here: ______
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Announcements
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WhatÕs new?
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Syllabus
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Course Schedule
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Course Assignments
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Course Materials and Resources
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Submitting Course Work
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Online Surveys
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Online Quizzes
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Gradebook
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Course Email
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Discussion Forums
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Chat Rooms
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Links
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Other Ð Please describe:
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APPENDIX B
Institutional Review Board Materials
Consent
Form
IRB
Approval Letter from Indiana State University
IRB
Approval Letter from Rose-Hulman Institute of Technology
March 17, 2005
Dissertation Research
Project
You are being invited to participate in a graduate research
project to study adopters and nonadopters of course management systems. The
research is in support of a dissertation study titled: ADOPTER CHARACTERISTICS
AND TEACHING STYLES OF FACULTY ADOPTERS AND NONADOPTERS OF A COURSE MANAGEMENT
SYSTEM. The study will address the following research questions:
- What are the differences in
adopter characteristics and teaching styles of adopters and nonadopters of
the ANGEL course management system?
- What are the reasons for
adoption and nonadoption of the ANGEL course management system?
- How do adopters use the ANGEL
course management system?
This study will be conducted
during an interview with each volunteer that lasts approximately ten minutes.
During that time, you will be asked to complete three short survey forms and
answer several questions verbally.
There are no known risks if
you decide to participate in this research study. There are no costs to you for
participating in the study. You will be offered the results of your Teaching
Styles Inventory, along with a description of their meaning. While the other
information collected may not benefit you directly, the information learned in
this study will provide more general benefits.
This study is anonymous. No one will be able to identify you or your
answers, and no one will know whether or not you participated in the study.
Individuals from the Institutional Review Board may inspect these records. No
individual information will be disclosed in the dissertation.
Your participation in this
study is voluntary. You may decline to participate without penalty. You are
free to discontinue your participation at any time without penalty.
Please sign below to indicate your consent to participate in
this study.
_______________________________________ _____________
Signature Date
_______________________________________
Name (printed)
If you have any questions about your rights as a research
subject or if you feel youÕve been placed at risk, you may contact the Indiana
State University Institutional Review Board (IRB) by mail at 114 Erickson
Hall, Terre Haute, IN, 47809, by phone at
(812) 237-8217, or by e-mail at irb@indstate.edu.
