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Comparing Student Learning Styles in an Online
Distance Learning Class and an Equivalent On-Campus Class
by David P. Diaz and Ryan B. Cartnal, Cuesta Community College
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Abstract
Educators have, for many years, noticed that some students prefer certain methods of
learning more than others. These traits, referred to as learning styles, form a student's
unique learning preference and aid teachers in the planning of small-group and
individualized instruction. If optimal student learning is dependent on learning styles,
and these styles vary between distance and equivalent on-campus students, then faculty
should be aware of these differences and alter their preparation and instructional methods
accordingly.
The purpose of this study was to compare the student learning styles of two online health
education classes (with a total of 68 students) with an equivalent on-campus class (40
students). The Grasha-Riechmann Student Learning Style Scales (GRSLSS) were administered
to determine student social learning preferences in six learning style categories.
Students who enrolled in the distance education class were significantly more independent
learners than students in the equivalent on-campus class. Students enrolled in the
equivalent class were significantly more Dependent learners than the distance group).
Correlational analysis revealed that on-campus students displayed collaborative tendencies
that were positively related to their needs to be competitive and to be good class
citizens. Thus, on-campus students appeared to favor collaborative styles to the extent
that it helped them to obtain the rewards of the class. In contrast, online students were
willing and able to embrace collaborative teaching styles if the instructor made it clear
that this was expected, and gave them form and guidance for meeting this expectation.
Online students appeared to be driven more by intrinsic motives and clearly not by the
reward structure of the class.
Faculty who are putting a traditional course online, should consider administering a
student learning style inventory to both their distance and traditional students.
Knowledge of student learning preferences can aid faculty in class preparation, designing
class delivery methods, choosing appropriate technologies, and developing sensitivity to
differing student learning preferences within the distance education environment.
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Introduction
The idea that people learn differently is venerable and probably had its origin with the
ancient Greeks (Wratcher, Morrison, Riley & Scheirton, 1997). Educators have, for many
years, noticed that some students prefer certain methods of learning more than others.
These dispositions, referred to as learning styles, form a student's unique learning
preference and aid teachers in the planning of small-group and individualized instruction
(Kemp, Morrison & Ross, 1998, p. 40). Grasha (1996), has defined learning styles as,
"personal qualities that influence a student's ability to acquire information, to
interact with peers and the teacher, and otherwise participate in learning
experiences" (p. 41).
Blackmore (1996) suggested that one of the first things educators can do to aid the
learning process is to simply be aware that there are diverse learning styles in the
student population:
There are probably as many ways to "teach" as there are to learn. Perhaps the
most important thing is to be aware that people do not all see the world in the same way.
They may have very different preferences than you for how, when, where and how often to
learn.
While many instructors are aware that different learning styles exist, the application of
this knowledge is often inconsequential. Some faculty simply opt to utilize a wide variety
of teaching activities, hoping that they will cover most student learning preferences
along the way. This method, though expedient, may not be the most effective or systematic
way to address student learning preferences in the classroom. Many instructors think that
the same teaching methods that are effective in their traditional classes will also work
in distance learning settings. The underlying assumption is that students who enroll into
distance education classes will have the same learning preferences as students enrolled in
traditional classes. Also, faculty are assuming that teaching styles, and accompanying
classroom processes, are like a "master key" and thus appropriate for any
setting.
There is not an overabundance of research in the area of learning styles and distance
education. Most of the studies focus on the discovery of relationships between learning
styles and specific student achievement outcomes: drop rate, completion rate, attitudes
about learning, and predictors of high risk. One of the most popular learning style
inventories and one that is often used in distance learning research is the Kolb Learning
Style Inventory (LSI) (Kolb, 1986). Kolb's LSI measures student learning style preference
in two bipolar dimensions. Over time, learners develop a preference for either concrete
experiences when learning or a preference for engaging in abstract or conceptual analyses
when acquiring skills and knowledge. They also may emphasize interests in turning theory
into practice, i.e., active experimentation, or they may prefer to engage in reflective
thinking about their experiences, i.e., reflective observation (Dille & Mezack, 1991,
p. 27). James and Gardner (1995) described Kolb's LSI as a cognitive learning style mode.
Cognitive processes include storage and retrieval of information in the brain and
represent the learner's ways of perceiving, thinking, problem-solving and remembering (p.
20). Dille and Mezack (1991) used Kolb's LSI to identify predictors of high risk among
community college telecourse students. Successful students had lower scores on their
preferences for concrete experiences than did the non-successful students. Thus, since
distance learning courses often lead to social isolation, and require greater reliance on
independent learning skills, students with less needs for the concrete experience aspects
of learning may be expected to be better suited to the distance format. People with higher
scores on concrete experience tend to exhibit a greater sensitivity to feelings, and thus
would be expected to require more interactions with peers and the teacher. Successful
telecourse students also preferred to look for abstract concepts to help explain the
concrete experiences associated with their learning. That is, they wanted to know
"why" certain things happened in conceptual or theoretical terms. This more
abstract approach clearly favored success in the telecourse. Dille and Mezack concluded
that students who needed concrete experience and were not able to think abstractly were
more high-risk students in a telecourse.
Gee (1990) studied the impact of learning style variables in a live teleconference
distance education class. The purpose of the study was to examine the influence of student
learning style preference, in an on-campus or distance education remote classroom, on
student achievement in the following areas: course content, course completion rates, and
attitudes about learning. Both distance and on-campus groups were taught simultaneously by
the same instructor, received identical course content, and both groups met weekly. Gee
administered the Canfield Learning Styles Inventory (CLSI) (Canfield, 1980).
Students in the distance learning class who possessed a more independent and conceptual
learning style, had the highest average scores in all of the student achievement areas.
People with the lowest scores in student achievement in the distance learning course had a
more social and conceptual learning style. Students with both a social and applied
learning style performed much better in the on-campus class. The outcomes of the Gee study
suggested that successful distance education students favored an independent learning
environment while successful on-campus students showed a preference for working with
others. The relatively small sample of 26 students suggested that additional work is
needed to further explore this relationship.
An important question is raised by such research: "Are there differences in learning
styles between students who enroll into a distance education class and their equivalent
on-campus counterparts?" This question, no matter which way it is answered, holds
important strategic information for anyone interested in student success. If there are no
differences in learning styles, then it is likely that faculty can transfer the same types
of teaching/learning activities that have been successful for them in the traditional
environment, into the distance setting with similar success. This is providing that
sufficient sensitivity has been given to student learning styles in the first place, and
that sufficient thought has been given to how these methods will be transferred to the
distance education environment using current communications technologies. If there are
differences in learning styles between groups of students, then faculty must use learning
style information to aid their planning and preparation for delivery of distance education
activities. Sarasin (1998) noted that instructors should be willing to change their
teaching strategies and techniques based on an appreciation of the variety of student
learning styles. "[Teachers] should try to ensure that their methods, materials, and
resources fit the ways in which their students learn and maximize the learning potential
of each student" (p. 2).
Knowledge of student learning preferences can provide a bridge to course success in a
distance education mode. If optimal student learning is dependent on learning styles, and
these styles vary between distance and equivalent on-campus students, then faculty should
be aware of these differences and alter their preparation and instructional methods
accordingly. In any case, the first step in using learning style information to aid
instruction in a distance education setting is to first determine student learning styles.
Selecting a Learning Style Instrument
As educators consider transplanting their traditional courses into distance learning
settings, they should also consider assessing the learning styles of the students who
enroll. With a variety of learning style instruments in use, it is important to carefully
select an instrument according to the unique requirements of the distance learning
context. Three important factors to consider when selecting a learning style instrument
include: considering the intended use of the data to be collected, finding an instrument
and matching it to the intended use and, finally, selecting the most appropriate
instrument (James and Gardner, 1995). Other concerns include considering the underlying
concepts and design of the instrument, validity and reliability issues, administration
difficulties, and cost (p. 22).
One of the distinguishing features of most distance education classes is the absence of
face-to-face social interaction between students and teacher. It seems appropriate that an
inventory used in a distance education setting should address the impact of different
social dynamics on the learning preferences of the students. An example of this can be
seen in Gee (1990), who employed the Canfield Learning Styles Inventory (CLSI). The CLSI
demonstrated merit for use in distance learning studies since it attempted to measure
student preferences in environmental conditions such as student's need for affiliation
with other students and instructor, and the student's need for independence or structure.
These differing social dynamics represent one of the main differences between distance
learning and equivalent on-campus environments. However, in our opinion, the CLSI as well
as Kolb's LSI, create a narrow range of applicability for learning styles by limiting
learning preferences to one or two dimensions. This learning style
"stereotyping" may be convenient for statistical analysis, but is less helpful
in terms of teaching students about weaker or unused learning preferences. Further, the
Kolb LSI, which has been widely used, is primarily a cognitive learning preference
instrument, and does not specifically take into account social preference issues that
represent the key distinction between the distance and traditional classrooms.
Of the different learning style instruments, the Grasha-Reichmann Student Learning Style
Scales (GRSLSS) seems ideal for assessing student learning preferences in a college-level
distance learning setting. The GRSLSS (Hruska-Riechmann & Grasha, 1982; Grasha, 1996)
was chosen as the tool for determining student learning styles in the present study based
on criteria suggested by James and Gardner (1995). First, the GRSLSS is one of the few
instruments designed specifically to be used with senior high school and
college/university students (Hruska-Riechmann & Grasha, 1982). Second, the GRSLSS is a
relevant scale to use in a distance setting since it focuses on how students interact with
the instructor, other students, and with learning in general. Thus the scales address one
of the key distinguishing features of a distance class, the relative absence of social
interaction between instructor/student and student/student. Third, the GRSLSS promotes an
optimal teaching/learning environment by helping faculty design courses and develop
sensitivity to student/learner needs. Fourth, the GRSLSS promotes understanding of
learning styles in a broad context, spanning six categories. Students possess all of six
learning styles, to a greater or lesser extent. This type of understanding prevents
learning style stereotyping, and provides a rationale for pursuing personal growth and
development in the underused learning style areas. A brief discussion of each learning
style is included below.
Independent students prefer independent study, self-paced instruction, and would prefer to
work alone on course projects than with other students.
Dependent learners look to the teacher and to peers as a source of structure and guidance
and prefer an authority figure to tell them what to do.
Competitive students learn in order to perform better than their peers do and to receive
recognition for their academic accomplishments.
Collaborative learners acquire information by sharing and by cooperating with teacher and
peers. They prefer lectures with small group discussions and group projects.
Avoidant learners are not enthused about attending class or acquiring class content. They
are typically uninterested and are sometimes overwhelmed by class activities.
Participant learners are interested in class activities and discussion, and are eager to
do as much class work as possible. They are keenly aware of, and have a desire to meet,
teacher expectations.
The styles described by the GRSLSS refer to a blend of characteristics that apply to all
students (Grasha, 1996, p. 127). Each person possesses some of each of the learning
styles. Ideally, one would have a balance of all the learning styles, however most people
gravitate toward one or two of the learning style preferences. Learning preferences are
likely to change as one encounters new life and educational experiences. Grasha (1996),
and Dowdall (1991) also have suggested that particular teaching styles might encourage
students to adopt certain learning styles. Additional information on this issue is
provided in the Grasha and Yangarber (1999) article in this section.
Problem and Purpose
Student performance may be related to learning preferences, or styles as learners.
Students may also self-select into or away from distance learning classes based on their
learning preferences. As a result, student success in distance learning classes may
ultimately depend on understanding the learning style characteristics of the students who
enroll.
Since more online courses will invariably be offered in the future, some assurance must be
provided to the institution, the faculty and the students, that distance education will
meet expectations for a quality education. Not only will students expect an education that
is at equal in quality as that provided by traditional offerings, they will expect a
student-centered learning environment, designed to meet their individual needs. There have
been few studies on the relationship of learning styles to student success in a distance
learning environment, and none that the author is aware of have used the GRSLSS. The
purpose of this study was to compare the student learning styles of online, and equivalent
on-campus, health education classes using the GRSLSS.
Research Methods
The population for the current study included health education students in a medium-sized
(8,000-9,000 enrollment) community college on the central coast of California. The
distance education sample included students in two sections of health education offered in
an online format (N = 68). The comparison class was selected from four equivalent
on-campus sections of health education (N = 40) taught by the lead author. The online
distance students were taught according to the same course outline, used the same
textbook, covered the same lecture material, and took the same tests as the equivalent
on-campus students. Three main differences between on-campus and online groups were the
delivery mode for the lectures, the mode of teacher/student and student/student
communication, and the mode for the assignments. The distance classes reviewed multimedia
slides (Power Point presentations converted to HTML) and lecture notes online while the
equivalent classes heard instructor lectures and participated in face-to-face discussion.
The distance class made heavy use of a class web site and used a list serve and e-mail for
communication/discussion with other students and the instructor. The assignment load for
the distance class students consisted almost entirely of internet-based, independent
assignments while the equivalent class completed some online assignments but participated
most frequently in classroom discussion assignments and other non-internet assignments.
All 108 participants first reviewed the student cover letter that explained the nature of
the research and provided opportunity for informed consent. Next, the authors distributed
the GRSLSS and reviewed the instructions for completion of the inventory. The GRSLSS was
administered in a group setting during the second week of classes. As a result, the
"General Class Form" was used (the version used when the inventory is
administered at the beginning rather than the end of the course) to assess the initial
learning styles of the students. The inventory was self-scored by the student and raw
scores were obtained for each of the learning style categories. Inventories were reviewed
by the researchers for compliance with directions and for accuracy of scoring.
Research Outcomes
The present study compared social learning styles between distance education and
equivalent on-campus classes using the GRSLSS. The average or mean scores of the distance
learning class and the equivalent health education class on each of the six categories of
the GRSLSS are shown in Figure 1. Relatively larger differences in the average scores
between the two classrooms occurred for the Independent and the Dependent learning styles.
Compared to those students enrolled in the traditional classroom, the students in the
distance learning class had higher scores on the Independent learning style scale and
lower scores on the Dependent learning style scale. A statistical test (i.e., a t- test )
was used to determine if the differences in the scores between the Independent and
Dependent learning styles were due to chance.
The variations in average scores between the two styles were found to be statistically
significant and thus were not likely due to chance (p < .01). The variations in average
scores between the two classrooms on the Avoidant, Competitive, Collaborative, and
Participant learning styles were relatively small, and a statistical analysis using a
t-test revealed that they were not statistically significant. In order to examine the
patterns in the relationships among the learning styles within each class, the
associations among different combinations of styles were examined. This was done by
calculating the correlation coefficients associated with the combinations of the six
learning styles. The outcomes of this analysis are shown in Table 1 for the distance
learning and traditional classroom groups. In reading this table the reader is reminded
that a correlation coefficient varies from -1, 0, to +1 and that the degree to which it
deviates from zero in either direction reflects the strength of the relationship between
the two variables. The asterisks associated with some of the values indicate that the size
of the correlation was statistically significant and thus not due to chance.
Correlational analysis within the online group showed a negative relationship between the
Independent learning style, and the Collaborative and Dependent learning styles. In other
words, people who were more Independent in their learning styles also tended to be less
Collaborative and Dependent. A second important relationship (positive correlation) was
found between the Collaborative learning style and the Dependent and Participant learning
styles. That is, students who were more Collaborative in their learning styles also were
more Dependent and Participatory in their approach to learning.
In the equivalent on-campus group, significant positive correlations were found between
the Collaborative learning style and the Competitive and Participant styles. That is,
on-campus students who were collaborative also tended to be competitive and participatory
in the classroom. Finally, a positive correlation between the Competitive and Participant
styles of learning also was observed. Students who tended to compete also were "good
classroom citizens" and were more willing to do what the teacher wanted them to do.
Discussion
Gibson (1998) has challenged distance education instructors to "know the
learner" (p. 140). She noted that distance learners are a heterogeneous group, and
that instructors should design learning activities to capitalize on this diversity (p.
141). Since the dynamic nature of the distance population precludes a "typical"
student profile (Thompson, 1998, p. 9), instructors should continually assess student
learner characteristics. The broad range of GRSLSS scores in the present study
demonstrated the diversity of learning preferences of both groups and illustrates the
dynamic nature of distance student characteristics as noted by Thompson. An instructor
using the present data could plan learning opportunities that would emphasize the learning
preferences of each of the commonly preferred learning styles (i.e., Independent,
Dependent, Collaborative, and Participant), thus matching teaching strategies with
learning styles.
Of particular interest were the significant differences between the groups in the
Independent and Dependent categories. The distance students more strongly favored
independent learning styles. It is not surprising that students who prefer independent,
self-paced instruction would self-select into an online class. It may be that the distance
education format appealed to students with independent learning styles, and that
independent learning preferences are well suited to the relative isolation of the distance
learning environment. This interpretation would agree with Gee (1990) who noted that
successful telecourse students favored an independent learning style. This also agrees
with James and Gardner (1995) who suggested that distance education students who favored
reliance on independent learning skills would be more suited to a distance format. As a
result of these significant differences, instructional strategies in the distance class
should emphasize relatively more independent, and fewer dependent learning opportunities.
This approach has practical significance given that instructors often complain of too
little "class time" to devote to learning objectives. Armed with learning style
data, instructors can more efficiently allocate course instructional time to various
learning activity types.
Not only were online students more independent than the on-campus students, but their
independent learning preferences were displayed in a way that was negatively related to
how dependent and collaborative they were. That is, the independence displayed by online
learners was not tied to needs for external structure and guidance from their teacher
(dependence), or for a need to collaborate with their classmates. Thus, the online
students can be described as "strongly independent," in that they match the
stereotype of the independent learner in terms of autonomy and the ability to be
self-directed. Self-direction and independence was facilitated in the online course by
offering students flexible options to shape their learning environment. The lead author
utilized self-paced, independent learning activities that allowed students to choose from
a menu of online "cyber assignments" based on their personal interests and the
relevance of the assignments to their own life situations. Students chose their own
assignments and completed the assignments by the deadlines posted online at the class web
site.
Students in the equivalent on-campus class were significantly more Dependent learners than
the distance group. Since Dependent learners prefer structure and guidance in the learning
setting, it is not difficult to understand why dependent learners might view the isolation
and need for self-reliance in a distance education environment with some apprehension. The
low level of independence displayed by on-campus students was not related to any other
aspects of their styles as learners. Thus, independence was clearly a weaker learning
preference for traditional class students.
The online students also displayed collaborative qualities in their styles as learners
that were related to their need for structure (dependence), and their willingness to
participate as good class citizens (Participant dimension). This correlation demonstrated
that, though online students prefer independent learning situations, they are willing and
able to participate in collaborative work if they have structure from the teacher to
initiate it. In his online class, the lead author has used "list serves" and
"threaded discussion" areas to promote collaboration among distance students.
However, in the past, the author designed collaborative activities among students that
required students to initiate peer contact, and to conduct the collaboration with a
minimum of teacher-provided structure and support. Based on the findings of the current
study, it is apparent why this strategy failed: Online students will apparently respond
well to collaborative activities, but only if sufficient structure and guidance is
provided by the instructor. The mistake made by the author was that he assumed that online
students would be self-directed, and autonomous, regardless of the type of learning
activity. In contrast, the traditional class students had collaborative tendencies that
were related to their needs to be competitive, and to be good classroom citizens. In other
words, they were interested in collaboration to the extent that it helped them to compete
favorably in the class, and to meet the expectations of their teachers. Thus,
collaboration was tied to obtaining the rewards of the class, not to an interest in being
collaborative per se.
Average Avoidant and Competitive learning style scores indicated that these learning
preferences were favored to a lesser degree by both groups. It was interesting that,
though we live in a highly competitive society, neither the online or equivalent on-campus
students really preferred a Competitive learning environment relative to other styles of
learning. However, the on-campus students appeared to favor competitiveness if it was
clear that such competitiveness was expected (i.e., thus the relationship of Competitive
and Participant styles).
Instructors can also use learning style data to help them design "creative
mismatches" where students can experience their less dominant learning style
characteristics in a less threatening environment (Grasha, 1996, p. 172). Designing
collaborative assignments for independent learners, or independent assignments for
dependent or collaborative learners, is appropriate and even necessary. Strengthening
lesser-preferred learning styles helps students to expand the scope of their learning,
become more versatile learners, and adapt to the requisites of the "real world"
(Sarasin, 1998, p. 38).
Learning styles were not the only differences between the distance and comparison groups
in this study. Demographic data indicated that the distance group had a higher percentage
of females (59%, 49%), students currently enrolled in under 12 units (66%, 50%), students
who had completed 60 or more college units (12%, 1%), had completed a degree (12%, 7%),
and students above 26 years of age (36%, 6%). These characteristics agree with the general
profile of distance students as reported by Thompson (1998). Though it is tempting to
identify and depend on a "typical" distance student profile, it is likely that
the dynamic nature of distance education in general will keep student characteristics a
moving target. Thus, distance education instructors should continually monitor student
characteristics.
Conclusions
The authors concluded that local health education students enrolled in an online class are
likely to have different learning styles than equivalent on-campus students. Online
students were more independent, and on-campus students more dependent, in their styles as
learners. The on-campus students seemed to match the profile of traditional students who
are willing to work in class provided they can obtain rewards for working with others, and
for meeting teacher expectations. Online students appeared to be driven more by intrinsic
motives and clearly not by the reward structure of the class.
One of the limitations of this study was the utilization of a non-probability
(convenience) sampling technique. Non-probability sampling is used when it is impossible
or impractical to use random sampling techniques. This is the case in a large portion of
educational research. While still valid, the results should not be over-generalized. The
authors have demonstrated a real and substantial difference in learning styles between
distance, and equivalent on-campus, health education students at their institution.
Before faculty rush to find out the effects of learning styles on student outcomes, they
should first address the issue of whether learning style differences exist at all. The
results of this study should send an important notice to faculty who are teaching their
traditional courses in a distance mode, that there may be drastic learning style, as well
as other characteristic differences between distance and traditional students that
warrants consideration.
As the World Wide Web continues to become an important medium for educational delivery,
more and more courses will be offered in an online format. Though faculty may attempt to
utilize the same teaching methods in a distance environment that they would employ in an
equivalent on-campus class, the data from the current study suggest that faculty will
encounter significantly different learning preferences as well as other different student
characteristics. Thus, faculty may want to employ learning style inventories, as well as
collect relevant demographic data, to better prepare for distance classes and to adapt
their teaching methods to the preferences of the learners.
Faculty should use social learning style inventories and resulting data for the purpose of
facilitating class preparation, designing class delivery methods, choosing educational
technologies, and developing sensitivity to differing student learning preferences within
the distance education environment. Future field-based research should replicate the
current study in different institutions and disciplines.
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