Comparing Student Learning Styles in an Online
Distance Learning Class and an Equivalent On-Campus Class
David P. Diaz and Ryan B. Cartnal
Cuesta Community College
Bibliographical reference: Diaz, D. P., & Cartnal, R. B. (1999). Students' learning styles in two classes: Online distance learning and equivalent on-campus. College Teaching 47(4), 130-135.
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 (N = 68) with an equivalent on-campus class (N = 40). The Grasha-Riechmann Student Learning Style Scales (GRSLSS) was 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 (p < .01). Students enrolled in the equivalent class were significantly more Dependent learners than the distance group (p < .01).
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.
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.
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.
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.
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.
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.